Asset Management and IOT Device for Refrigerated Appliances

ABSTRACT

An asset management system includes an asset management network that connects various types of refrigeration appliances to an asset manager. At the device layer, IOT devices are connected to the refrigeration appliances and have edge compute capability, including a backup power management function, and a modem for network communication. IOT device ports directly integrate with sensors, switches, and/or serial data controllers of refrigeration appliances. Push notification alarms are initiated on the edge of the network by IOT devices. The network tags operating data by appliance serial, and the asset manager stores the data using serial numbers as primary keys. The asset manager streams data from numerous appliances and uses event driven processing to promptly respond to edge alarms. The operating data keyed by serial number is enriched with proprietary data organized by appliance type and is used to develop new predictive models by appliance type.

CROSS-REFERENCE TO RELATED APPLICATION

This Application is a continuation of International PCT Application No.PCT/US23/29015, Jul. 28, 2023, which claims priority to U.S. ProvisionalPatent Application No. 63/393,092, filed Jul. 28, 2022, each of which ishereby incorporated by reference in its entirety.

FIELD

This disclosure generally pertains to remote asset management systemsfor refrigeration appliances and internet of things (IOT) devices thatfacilitate connecting refrigeration appliances to such remote assetmanagement systems.

BACKGROUND

Refrigeration appliances are in wide residential and commercial use.Such refrigeration appliances include coolers, freezers, and ice makers.Refrigeration appliances are one element of the cold chain. There issignificant interest in developing network-based remote asset managementtechnologies for remotely monitoring and controlling assets throughoutthe cold chain, including refrigeration appliances. The industrybelieves that cold chain asset management solutions could be used toimprove product safety and reduce product loss due to equipment misuseand malfunction. Further, it has been proposed to combine cold chainmonitoring with big data techniques to identify patterns, anomalies, andusage trends, in order to predict maintenance needs, optimize energyconsumption, and take proactive action to avoid equipment failure.

Most state of the art asset management systems for refrigerationappliance monitoring utilize internet of things (IOT) accessories. TheseIOT devices are typically developed by the tech industry and thereforeare designed around tech industry standards and assumptions. The typicalIOT device setup includes one or more accessory sensors that areconnected (wired or wirelessly) to a gateway device near the appliance.The gateway device acts as a bridge between the sensors and a remoteasset manager. The industry has considered various types ofcommunication protocols for gateway devices, including wired Ethernet,Wi-Fi, long-range radio (e.g., LoRaWAN), and cellular (e.g., GSM, CDMA,UMTS, LTE, URLLC, 2G, 3G, 45, 5G). Because IOT solutions forrefrigeration appliance monitoring have largely been developed usingtech industry protocols and assumptions, communication between gatewaydevices and asset monitoring networks employ tech industry standards.

SUMMARY

In one aspect, an asset management system for a plurality ofrefrigeration appliances comprises a remote asset manager. Eachrefrigeration appliance has a serial number. An asset management networkis connected to the asset manager. A plurality of IOT devices areconnected to the refrigeration appliances and receive operating datafrom the refrigeration appliances. Each of the IOT devices has a modemconnecting the IOT device to the asset management network. Each modemhas a modem device ID. Each of the IOT devices is configured to transmitthe operating data to the asset manager via the asset managementnetwork. The asset manager is configured to receive from the assetmanagement network data objects containing operating data for respectiverefrigeration appliances. Each data object is tagged with the serialnumber of the respective refrigeration appliance. The cloud-based assetmanager is configured to store the operating data in a database usingthe serial number of the refrigeration appliance as a primary key forthe database.

In another aspect, a processor-executable method of connecting arefrigeration appliance to an asset manager in an asset managementsystem for a plurality of refrigeration appliances comprises receiving aweb page request. The web page request includes a device ID for an IOTdevice. It is determined, based on the device ID, that the IOT devicehas not been bound to a refrigeration appliance. In response toreceiving the web page request for the IOT device and determining thatthe IOT device has not been bound to a refrigeration appliance, a webform that includes a field for input of a serial number of arefrigeration appliance is returned. After returning the web form, inputof the serial number of the refrigeration appliance is received by theweb form. In response to receiving input of the serial number of therefrigeration appliance to the web form, the IOT device is bound to therefrigeration appliance in the asset management system.

In another aspect, a method for operating an asset management system fora plurality of refrigeration appliances comprises transmitting firstoperating data for a refrigeration appliance from a first IOT device toan asset management network. The first IOT device has a first device ID.The first operating data is tagged with a serial number for therefrigeration appliance. The tagged first operating data is receivedfrom the asset management network at an asset manager. The tagged firstoperating data is stored by the asset manager in a time series databasewith the serial number for the refrigeration appliance as a primary key.Subsequently, a second IOT device having a second device ID is bound tothe refrigeration appliance. Second operating data for the refrigerationappliance is transmitted from the second IOT device to the assetmanagement network. The second operating data from the refrigerationappliance is tagged with the serial number for the refrigerationappliance. The tagged second operating data is received from the assetmanagement network at the asset manager. The tagged second operatingdata is stored by the asset manager in said time series database. Thetime series database has the serial number for the refrigerationappliance as a primary key. The time series database is seamlesslymaintained by the asset manager for the refrigeration appliance for boththe first operating data transmitted by the first IOT device and thesecond operating data transmitted by the second IOT device.

In another aspect, an asset management system for a plurality ofrefrigeration appliances comprises a plurality of IOT devices. Eachrefrigeration appliance has a serial number. Each IOT device comprises amodem that has a device ID and is emblazoned with a machine readablecode encoding a web address with the respective device ID. Each IOTdevice is configured to be bound to a respective one of therefrigeration appliances and is configured to use the modem to transmitoperating data from the respective refrigeration appliance. A databroker is configured to receive the operating data from the plurality ofIOT devices and transmit structured data objects containing theoperating data. Each structured data object is tagged by the serialnumber of a source refrigeration appliance. The data broker is furtherconfigured to receive web page requests from one or more client devicesentering said web addresses including the respective device IDs into webbrowsers. In response to each web page request, the data broker isconfigured to determine based on the included device ID whether the IOTdevice has been bound to a refrigeration appliance. In response toreceiving a web page request and determining based on the includeddevice ID that the IOT device has not been bound to a refrigerationappliance, the data broker is further configured to redirect the webpage request to another web address for a web form including a field forinputting a serial number of a refrigeration appliance to bind the IOTdevice to the refrigeration appliance with the serial number input intothe field. In response to receiving a web page request and determiningbased on the included device ID that the IOT device has been bound to arefrigeration appliance, the data broker is further configured toredirect the web page request to another web address for a public webpage for displaying operating data for the respective refrigerationappliance. An asset manager is configured to stream the structured dataobjects from the data broker, parse the streamed structured data objectsby serial number of the source refrigeration appliance, and store theoperating data contained in the structured data objects in a time seriesdatabase using the serial numbers of the source refrigeration appliancesas primary keys.

In another aspect, an asset management system for a plurality ofrefrigeration appliances comprises a plurality of IOT devices. Eachrefrigeration appliance is one of a plurality of different refrigerationappliance types. Each IOT device is bound to a respective refrigerationappliance. The refrigeration appliance comprises a modem configured fornetwork communication and one or more I/O ports configured for wiredconnection to the respective refrigeration appliance. An asset manageris configured to receive operating data from source refrigerationappliances transmitted via the modems of the plurality of IOT devices.The asset manager is configured to parse the operating data by sourcerefrigeration appliance and store the operating data in a time seriesdatabase for each source refrigeration appliance. An OEM databasecontains proprietary OEM data organized by refrigeration appliance type.The asset manager is configured to read the proprietary OEM data fromthe OEM database. The asset manager is configured to act on theoperating data for at least one refrigeration appliance of a specifiedrefrigeration appliance type by combining the operating data for said atleast one refrigeration appliance of the specified refrigerationappliance type with the proprietary OEM data for said specifiedrefrigeration appliance type.

In another aspect, an asset management system for a plurality ofrefrigeration appliances comprises a plurality of IOT devices. Eachrefrigeration appliance has a unique serial number and is one of aplurality of different refrigeration appliance types. Each IOT device isbound to a respective refrigeration appliance and comprises a modemconfigured for network communication and one or more I/O portsconfigured for wired connection to the respective refrigerationappliance. An asset manager is configured to receive operating data fromsource refrigeration appliances transmitted via the modems of theplurality of IOT devices. The asset manager is configured to parse theoperating data by the source refrigeration appliance and store theoperating data for each source refrigeration appliance in a time seriesdatabase. The asset manager is further configured to aggregate operatingdata in the time series database by appliance type. The asset manager isfurther configured to obtain one or more models of an appliance typebased on the operating data aggregated by appliance type.

In another aspect, an IOT device for connecting a refrigerationappliance of any of a plurality of different refrigeration appliancetypes to a remote asset management system comprises a plurality of lowvoltage I/O ports. Each low voltage I/O port is configured to beselectively mated to a cable connector of the refrigeration appliance toterminate a low voltage cable connected to one of a prefabricated sensorof the refrigeration appliance and a low-voltage switch of therefrigeration appliance. A serial data port is configured to beselectively mated to a cable connector of the refrigeration appliance toterminate a serial data cable connected to a prefabricated serial datacontroller of the refrigeration appliance. An edge computing device isoperatively connected to each of the plurality of low voltage I/O portsand the serial data port. The edge computing device has a processor anda memory configured for storing a refrigeration appliance type-specificappliance control profile to configure the processor for readingoperating data from respective ones of the low voltage I/O ports and/orserial data port to which one or more low voltage cables and/or a serialdata cable of a refrigeration appliance are connected. A modem isconfigured for network communication. The edge computing device isconfigured to control transmission of operating data read from therespective ones of the low voltage I/O ports and/or serial data port tothe asset management system via the modem.

In another aspect, a method of connecting an IOT device to arefrigeration appliance and an asset management system comprisesplugging one or more cable connectors of the refrigeration applianceinto one or more of (i) a plurality of low voltage I/O ports of the IOTdevice and (ii) a serial data port of the I/O device. The IOT device isconnected to a main power source. A modem of the IOT device is connectedto an asset management network of the asset management system. Aprocessor-executable control profile is loaded from the asset managementsystem onto memory of an edge computing device of the IOT device toconfigure a processor of the edge computing device for reading operatingdata from said one or more of (i) the plurality of low voltage I/O portsof the IOT device and (ii) the serial data port of the I/O device. Theprocessor transmits said operating data to the asset management systemvia the modem.

In another aspect, an IOT device for connecting a refrigerationappliance to a remote asset management system comprises an edgecomputing device. The edge computing device is configured to beoperatively connected to the refrigeration appliance for readingoperating data from the refrigeration appliance. A modem is configuredfor network communication. The edge computing device is configured tocontrol transmission of operating data read from the refrigerationappliance to the asset management system via the modem. A main powerport is configured to connect the IOT device to main power. A backuppower supply is configured to power the IOT device in a main poweroutage. The edge computing device is configured to recognize loss ofmain power at the main power port, and in response, conduct a powerfailure routine in which the edge computing device draws power from thebackup power supply. A power outage alarm notification is transmitted tothe asset management system. Operating data is subsequently sampled fromthe refrigeration appliance during a low power time interval withouttransmitting the sampled operating data to the remote asset managementsystem. After the lower power time interval elapses, the IOT device isautomatically put in a sleep mode in which the edge computing deviceceases sampling the operating data from the refrigeration appliance. Thesampled operating data from the low power time interval is automaticallyreported via the modem to the asset management system when restored mainpower is detected at the main power port.

In another aspect, a processor-executable method for using an IOT deviceof a refrigeration appliance comprises drawing power from a main powersupply. Operating data from the refrigeration appliance is periodicallysampled and reported to an asset management system via a modem. A lossof power from the main power supply is recognized. In response torecognizing the loss of power from the main power supply, power is drawnfrom a backup power supply. While drawing power from the backup powersupply, a power outage alarm notification is transmitted to the assetmanagement system via the modem. The operating data from therefrigeration appliance is periodically sampled during a low power timeinterval. The operating data is refrained from being transmitted to theremote asset management system. After the lower power time intervalelapses, sampling the operating data from the refrigeration appliancesceases. The sampled operating data is automatically reported from thelow power time interval to the asset management system after power fromthe main power supply is restored.

In another aspect, an asset management system for refrigerationappliances comprises a plurality of IOT devices. Each IOT device has anedge computing device and a modem for connecting the IOT device to anasset management network. Each TOT device is connected to a respectiverefrigeration appliance. The edge computing device is configured forsampling operating data from the respective refrigeration appliance at asampling frequency. The edge computing device is further configured fortransmitting the operating data to the asset management network via themodem at a transmission frequency less than the sampling frequency. Theedge computing device is further configured to analyze the sampledoperating data on an edge of the asset management network for detectingan alarm condition in the operating data. The edge computing device isfurther configured to immediately transmit an alarm indication to theasset management network via the modem when the alarm condition isdetected in the operating data. The edge computing device is configuredto transmit the alarm indication asynchronously from the transmissionfrequency. An asset manager is in communication with the plurality ofIOT devices via the asset management network. The asset manager isconfigured to receive a data stream from the IOT devices including theoperating data and the alarm indications. The asset manager isconfigured to recognize each alarm indication as an event andimmediately conduct event driven processing to assess whether pushnotification is required and push one or more notifications to one ormore users when push notification is required.

In another aspect, a method of using an asset management system for aplurality of refrigeration appliances comprises sampling at a samplingfrequency operating data from each refrigeration appliance at an edgecomputing device of a respective IOT device. Each refrigerationappliance is bound to the respective IOT device having the edgecomputing device and a modem. The asset management system includes anasset management network connecting the IOT devices to a remote assetmanager. The operating data from each refrigeration appliance istransmitted at a transmission frequency from the modem of the respectiveIOT device to the asset manager via the asset management network. Thetransmission frequency is less than the sampling frequency. A stream ofthe transmitted operating data from the IOT devices is received at theasset manager. The asset manager loads the operating data into a timeseries database. While performing said sampling and said transmitting,an alarm condition in the operating data is detected at the edgecomputing device of one of the IOT devices. An alarm indication isimmediately sent from the modem of said one of the IOT devices to theasset manager via the asset management network. Immediately sending thealarm indication is independent of transmitting operating data from themodem of said one of the IOT devices and is asynchronous with respect tothe transmission frequency for one of the IOT devices. The alarmindication is received at the asset manager. Event driven processing isused at the asset manager to determine that the alarm indicationrequires push notification. One or more notifications are immediatelypushed to one or more users of the refrigeration appliance bound to saidone of the IOT devices.

In another aspect, an IOT device for connecting a refrigerationappliance to a remote asset management system comprises an I/O port. TheI/O port is configured to connect to a cable connector terminating awire carrying a signal including an indication of a return airtemperature of the refrigeration appliance. An edge computing device isoperatively connected to the I/O port. The edge computing devicecomprises a processor and a memory storing processor executableinstructions configuring the processor for reading return airtemperature data from the I/O port at a sampling frequency. A modem isconfigured for network communication. The edge computing device isconfigured to control transmission of the return air temperature dataread from the I/O port to the asset management system via the modem. Theedge computing device is configured to periodically run a simulation ofa product temperature based on the return air temperature data read fromthe I/O port.

In another aspect, an asset management system for a plurality ofrefrigeration appliances comprises a remote asset manager configured toreceive a stream of operating data from the refrigeration appliances.The asset management system comprises a plurality of edge computingdevices. Each edge computing device is bound to a respectiverefrigeration appliance and configured to read an air temperature fromthe refrigeration appliance at a sampling frequency. Each edge computingdevice is further configured to simulate a product temperaturerepresentative of a product stored in the refrigeration appliance basedon the air temperature at a simulation frequency. Each edge computingdevice is further configured to transmit operating data to the assetmanager via an asset management network in transmissions transmitted ata transmission frequency. Each transmission includes the air temperatureread from the refrigeration appliance at the sampling frequency and theproduct temperature simulated at the simulation frequency.

In another aspect, a processor-executable method of monitoringtemperature-sensitive product stored in a refrigeration appliancecomprises sampling an air temperature of the refrigeration appliance. Aproduct temperature is simulated based on the air temperature. It isdetermined if the simulated product temperature exceeds a predefinedtemperature threshold for the temperature-sensitive product. Anotification is automatically pushed to a user associated with therefrigeration appliance within 60 seconds of determining the simulatedproduct temperature exceeds the predefined temperature threshold for thetemperature sensitive product.

Other aspects and features will become apparent hereinafter.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of an asset management system inaccordance with the present disclosure;

FIG. 2 is a perspective of an IOT device in accordance with the presentdisclosure;

FIG. 3 is a schematic block diagram of the IOT device;

FIG. 4 is a flow chart of a power management routine executed by the IOTdevice;

FIG. 5 is a schematic block diagram of an asset manager backend of theasset management system;

FIG. 6A is a screenshot of a fleet overview screen of a front end webapplication of the asset management system;

FIG. 6B is a screenshot of an asset list view of the front end webapplication of the asset management system;

FIG. 6C is a screenshot of an asset filter panel of the front end webapplication of the asset management system;

FIG. 6D is a screenshot of a customer view of the front end webapplication of the asset management system;

FIG. 6E is a screenshot of a fleet management view of the front end webapplication of the asset management system;

FIG. 6F is a screenshot of a store view of the front end web applicationof the asset management system;

FIG. 6G is a screenshot of an operating data profile part view of thefront end web application of the asset management system;

FIG. 6H is a screenshot of an alarm profile part view of the front endweb application of the asset management system;

FIG. 6I is a screenshot of a user view of the front end web applicationof the asset management system;

FIG. 7 is a flow chart illustrating steps and decision points of anappliance binding process of the present disclosure;

FIG. 8 is a screen shot of a public asset binding page generated on aclient device during the appliance binding process of FIG. 7 ;

FIG. 9 is a screen shot of a public refrigeration appliance data pagedisplayable on a client device after the refrigeration appliance isbound to an IOT device;

FIG. 10 is a flow chart illustrating steps of a process for seamlessreplacement of an IOT device in accordance with the present disclosure;

FIG. 11 is a schematic illustration of an edge processing routine thatcan be conducted by an IOT device in accordance with the presentdisclosure;

FIG. 12 is a schematic illustration similar to FIG. 11 but showing aparticular implementation of the edge procession routine used forproduct temperature simulation;

FIG. 13 is a flow chart illustrating the steps and decision points of abackend process conducted by the backend of the asset management system;

FIG. 14 is a schematic illustration of a data enrichment process thatcan be conducted using the asset management system;

FIG. 15 is a schematic illustration similar to FIG. 14 but showing aparticular implementation in which the data enrichment process is usedfor compressor life prediction;

FIG. 16 is a schematic illustration similar to FIG. 14 but showing aparticular implementation in which the data enrichment process is usedfor temperature simulation;

FIG. 17 is a schematic illustration of a process for obtainingpredictive models based on operating data stored in the asset managerbackend; and

FIG. 18 is a schematic illustration of a machine learning process forrecognizing and acting on patterns in refrigeration appliance operatingdata stored in the asset manager backend.

Corresponding parts are given corresponding reference numbers throughoutthe drawings.

DETAILED DESCRIPTION

This disclosure generally pertains to devices, systems, and methodsproviding IOT and asset management solutions for refrigerationappliances. In particular, this disclosure pertains to IOT and assetmanagement solutions that are intended elevate the importance of therefrigeration appliance (the asset), as opposed to the accessory gatewaydevice, in network communications. The inventor believes that theprimacy of tech industry protocols and assumptions in existing IOTsolutions for refrigeration appliances has inherent drawbacks, many ofwhich can be overcome by employing one or more aspects of the presentdisclosure.

Fundamentally, any remote asset management system for refrigerationappliances employs some type of network gateway device in the vicinityof the appliance. The gateway device comprises a modem configured with aunique device identifier (“device ID,” e.g., an IMEI for a cellularmodem, a DevEUI and DevAddr for a LoRaWAN modem, an MAC address forWi-Fi devices, etc.) which distinguishes that particular modem from allother modems. The modem is standardly configured to tag itstransmissions with the device ID as a source identifier and to receivedata from the network by address to its device ID. Because this is howmodems are configured as standard, IOT solutions developers haveconventionally used the modem device ID for the gateway device as thefundamental address for IOT communications. So for a refrigerationappliance IOT application, the gateway device will use the modem deviceID as a source signifier when transmitting appliance data to a remoteasset manager. Conversely, the remote asset manager will address anycommands issued to the appliance using the device ID for the modem ofthe IOT gateway device.

In a conventional asset management system with many refrigerationappliances under management, the remote asset management system will usethe modem device ID, which is the source signifier in every packet ofdata it receives, as the primary key in the data structures itmaintains. In other words, because modem device ID is the sourcesignifier for incoming data, the asset management system organizes databy modem device ID.

The inventor believes that addressing IOT communications forrefrigeration appliances by modem device ID and organizing data by modemdevice ID has fundamental drawbacks. At base, this approach places thefocus on the wrong entity. In an IOT asset management system forrefrigeration appliances, the inventor's view is that the focus ought tobe on the refrigeration appliances themselves—not the gateway devicesthat are essentially accessories to the refrigeration appliances.Moreover, the inventor believes that the misplacing the focus on thegateway devices instead of the refrigeration appliance has severalconcrete drawbacks to IOT implementation for refrigeration appliances.

As one example, associating information with the gateway device's modemID creates data segmentation problems when multiple gateway devices arerequired for the same refrigeration appliance. For example, if a firstgateway device for a given refrigeration appliance expires and must bereplaced by a second gateway device, an asset management system thatmisplaces the focus onto the modem device ID will produce two separatedatabase records, one referencing the modem ID of the first gatewaydevice and the other referencing the modem ID of the second gatewaydevice. This creates difficulties when there is a need to evaluateoperating data that spans the use of the first and second gatewaydevices.

Another problem with keying IOT communications and time series data tothe modem device ID is that it adds complexity to using the assetmanagement system for fleet management and appliance improvement. Theinventor believes that remote monitoring has useful applications forrefrigeration appliances when the aggregate operating data from numerousappliances on the network can be parsed by known characteristics of theappliance (e.g., by appliance model number, by appliance manufacturingyear, by a particular type of component that is included in theappliance, etc.). For example, as will be described in further detailbelow, the inventor envisions that big data techniques can be applied toidentify trends and anomalies for subsets of the appliances with similarcharacteristics. Additionally, the inventor expects that there will beopportunities to issue commands (e.g., firmware updates, controlparameter updates, etc.) to subsets of the appliance on the network withcommon characteristics. But by keying network communications and dataorganization to the modem ID instead of the refrigeration appliance, itis much more difficult to leverage these opportunities.

Again, because existing IOT solutions for refrigeration appliances havebeen developed using tech industry protocols and assumptions,communication between gateway devices and asset monitoring systemsemploy the tech industry standard of addressing and keying based onmodem ID. The inventor has a fundamentally different perspective on IOTsystem development, namely, that of a refrigeration appliancemanufacturer. Accordingly, as will be explained in further detail below,instead of addressing communications and organizing operating dataaccording to modem device IDs, the present disclosure provides IOTsystems that address communications and key operating data torefrigeration appliance serial number.

Because IOT solutions for refrigeration appliance monitoring havelargely been developed by the tech industry, tech industry standardshave been employed. The tech industry is inherently skeptical ofaddressing network communications by any identifier that is notrecognized by an industry-wide communication standard (e.g., GSM, CDMA,LoRaWAN, etc.) to be uniquely indicative one particular device. But theinventor recognizes that refrigeration appliance manufacturers arewell-positioned to ensure that each individual appliance is given aunique serial number distinct to that device. Moreover, therefrigeration appliance manufacturer is in position to control thenomenclature of its serial numbers so that the serial numbers provideinformation about the type or characteristics of the appliance that canbe parsed for aggregated data analysis. Hence, the inventor believesthat the serial number of the refrigeration appliance can be a suitableidentifier to use for addressing IOT communications and organizingoperating data.

Another downside of tech industry development of IOT solutions forrefrigeration appliances is apparent at the device level. ConventionalIOT solutions for refrigeration appliances comprise a gateway devicethat is connected to one or a small number of accessory temperatureprobes. These temperature probes are installed in the refrigerationappliance as relatively haphazard retrofits. Their locations andreadouts do not correlate to the temperature readings and other sensorinputs that control the refrigeration appliance as standard. Theinventor believes that it is preferable to configure the IOT device tointerface directly with the sensors, switches, and controls (broadly,prefabricated or designed-in hardware) that are native to therefrigeration appliance. Original equipment manufacturers (OEMs) use thenative sensors, switches, and controls during product design testing,regulatory testing, energy testing, or the like. Accordingly, theinventor believes that interfacing with the very same sensors, switches,and controls that were used during testing enables the application ofrich proprietary datasets that original equipment manufacturers (OEMs)develop during testing. The tech industry developing IOT solutions lacksaccess to the rich data sets available to refrigeration appliance OEMsand thus has not placed an emphasis on taking readings from nativeappliance equipment. Moreover, there are substantial challenges withdoing so because the equipment from one refrigeration appliance to thenext varies greatly. It is much easier for the tech industry to providea one-size- fits-all approach in which a universal gateway deviceconnects to one or more accessory temperature probes that are placedinto the refrigeration appliance as retrofits.

Referring to FIG. 1 , an exemplary embodiment of an asset managementsystem for refrigeration appliances is generally indicated at referencenumber 10. The asset management system 10 includes a plurality ofrefrigeration appliances 11A-11 n, an IOT device 12 for eachrefrigeration appliance, a cloud-based asset manager 14 in communicationwith the IOT devices 12, an optional cloud-based data broker 16 forbrokering communications between the IOT devices and the asset manager,and one or more networks 18, 19 for facilitating communication acrossthe asset management system. In the illustrated embodiment, the networks18, 19 and data broker 16 collectively form an asset management network20 for connecting the refrigeration appliances 11A-11 n to the assetmanager 14. In the network architecture, the IOT devices 12 are locatedon the edge of the asset management network 20 for connecting theappliances 11A-11 n to the network. The asset management system 10 isbroadly configured to facilitate remote monitoring and/or control of therefrigeration appliances 11A-11 n by the cloud-based asset manager 14.That is, the asset manager 14 comprises processor-executable softwarestored in memory and executed by one or more computer processors (e.g.,server processors, cloud service processors, etc.) that are physicallyremote from the appliances 11A-11 n. By contrast, the IOT devices 12 arelocated in the immediate vicinity of their respective refrigerationappliances 11A-11 n and connected to the appliances by wires.

In the illustrated example, the refrigeration appliances include acommercial cooler 11A, a commercial freezer 11B, a residentialrefrigerator 11C, a residential ice maker 11D, and a commercial icemaker 11E. The appliances 11A-11E are intended to provide a schematicillustration of how the asset management system 10 is configured toconnect numerous different refrigeration appliance types (e.g., modelnumbers) to the asset manager 14. For example, as will be explained infurther detail below, the asset management system 10 is configured toconnect to legacy refrigeration appliances lacking digitalmicrocontrollers or computers, as well as to more modern refrigerationappliances that are controlled by local digital controllers (e.g.,RS-485 Mod-Bus control boards). In FIG. 1 , an additional refrigerationappliance 11 n is shown schematically to represent how the assetmanagement system 10 is configured to connect any number ofrefrigeration appliances to the asset manager 12. In an exemplaryembodiment, the asset management system 10 includes numerousrefrigeration appliances 11 n of different refrigeration appliancetypes, with many instances of each appliance type connected to thenetwork 20. This allows for rich assessment of appliance operating databy appliance type.

In certain implementations, it is contemplated that asset managementsystems 10 in the scope of this disclosure are configured for massivescale and distribution. For example, in one or more embodiments thereare at least 1,000 IOT devices 12 connecting at least 1,000refrigeration appliances 11 n to the asset management network (e.g.,there are at least 10,000 IOT devices 12 connecting at least 10,000refrigeration appliances 11 n to the asset management network, at leastat least 100,000 IOT devices 12 connecting at least 100,000refrigeration appliances 11 n to the asset management network, at least500,000 IOT devices 12 connecting at least 500,000 refrigerationappliances 11 n to the asset management network, or at least at least1,000,000 IOT devices 12 connecting at least 1,000,000 refrigerationappliances 11 n to the asset management network). As will be explainedin further detail below, the asset manager 14 is configured toeffectively parse incoming data from the massively distributedrefrigeration appliances 11 n and both (1) maintain a rich time seriesdatabase organizing the incoming operating data by refrigerationappliance serial number and (2) immediately act on alarm indicationscontained in the incoming data.

Suitably, each refrigeration appliance 11A-11 n on the asset managementsystem has a unique serial number. As will be described in furtherdetail below, the asset management system 10 is configured so that thecloud-based asset manager 14 interacts with the refrigeration appliances11A-11 n by reference to the appliance serial number. The assetmanagement system 10 is essentially agnostic to the modem device IDs ofthe IOT devices 12. In this way, the asset management system has anasset-centric architecture and communication profile. The inventorbelieves that the asset-centric architecture provides for better assetmanagement than conventional IOT asset management systems, which usemodem device ID as the fundamental identifier for communication and dataorganization.

In one or more embodiments, many of the refrigeration appliances 11A-11n comprise one or more compression-driven refrigeration circuits, eachincluding an evaporator assembly, a compressor, a condenser assembly, adrier, an expansion device, and interconnecting tubing. Those skilled inthe art will be familiar with the basic components, functions, andoperations of these components in a compression-driven refrigerationcircuit. It is contemplated that refrigeration appliances could usesecondary refrigerant circuits and/or that other types of refrigerationsystems (e.g., thermoelectric refrigeration) could be used incombination with or independently of the compression-drive refrigerationcircuit.

Some refrigeration appliances 11 n in the scope of this disclosurecomprise digital controllers (e.g., a serial data controller such as aMod-Bus controller), while other refrigeration appliances in the scopeof this disclosure comprise basic (e.g., analog) thermostatic controlsystems. As explained more fully below, the asset management system 10is capable of interfacing with both types of refrigeration appliances.Refrigeration appliances in the scope of this disclosure will oftencomprise integrated sensors, such as integrated air temperature sensors(return air temperature sensor and/or supply air temperature sensor),integrated refrigerant sensors (e.g., liquid line temperature and/orpressure sensor, suction line temperature and/or pressure sensor), lowvoltage switches or sensors (e.g., door sensors), and/or ambient sensors(e.g., ambient temperature sensor and/or ambient humidity sensor). Inaddition, refrigeration appliances in the scope of this disclosure maycomprise a low voltage compressor relay to indicate with the compressoris running. Ice makers (e.g., ice maker 11D, 11E) will typicallycomprise a host of additional sensors and indicators. For additionalinformation about the kinds of sensors and indicators used in an icemaker, see co-assigned U.S. patent application Ser. No. 17/686,986,filed Mar. 4, 2022, which is hereby incorporated by reference in itsentirety.

As will be explained in further detail below, the asset manager 10 is tomonitors the refrigeration appliances 11A-11 n via the network 20,stores operating data from the refrigeration appliances in a time seriesdatabase, and analyzes the operating data. FIG. 1 schematicallyillustrates how the asset manager is configured to interface (via aremote network connection such as an interne connection) with a frontend web application 22 and a front end mobile application 24. The frontend applications 22, 24 enable users of the asset management system 10,such as owners or operators of some of the refrigeration appliances11A-11 n, to view operating data about their appliances in real time. Inaddition, the front end applications 22, 24 enable certain users toremotely control their refrigeration appliance assets 11A-11 n, such asby making changes to control parameters, activating low voltageswitches, and the like.

FIG. 1 also schematically illustrates how the asset manager isconfigured to issue notifications directly to one or more users 30 ofthe refrigeration appliances 11A-11 n independent of access to the frontend applications 22, 24. This represents the asset manager's ability topush alarm notifications to one or more designated users directly viaSMS message and email. As will be explained in further detail below, theasset management system 10 is able to guarantee that all alarms arepushed to the users 30 within 60 seconds of a sensor of therefrigeration appliance 11A-11 n detecting an operating parametercrossing an alarm threshold. The inventor believes this makes the pushnotifications sent to users 30 via asset manager 14 much more actionablethan prior push notification alarming systems for remote monitoring ofrefrigeration appliances.

In the illustrated embodiment, the asset manager 14 is further connectedto one or more auxiliary remote fleet managers 26. Each auxiliary fleetmanager 26 represents a proprietary cloud-based application or otherserver-hosted application employed by a proprietor of a large fleet ofrefrigeration appliances 11A-11 n, which make up a subset of the totalnumber n of refrigeration appliances in the asset management system 10.The auxiliary fleet manager 26 is designed and maintained by theproprietor of the fleet (e.g., the owner of the fleet, the operator ofthe fleet, the maintainer of the fleet, and/or the lessor of the fleet)to access the operating data stored in the asset manager 14 and applythe proprietor's own methods of analyzing and acting on the data.

In an exemplary embodiment, the asset manager 14 is further connected(via a remote network connection such as an internet connection) to oneor more original equipment manufacturer (OEM) databases 28. Each OEMdatabase 28 stores proprietary data about refrigeration assets organizedby asset type. For example, the OEM database 28 can store simulationdata, empirical testing data, and/or modeled data about the types ofassets produced by the OEM. The OEM database 28 therefore storesproprietary information about the appliances 11A-11 n. Such proprietaryinformation is typically only available to the OEM of the refrigerationappliances. As will be explained in further detail below, the assetmanager 14 is configured to access the proprietary OEM data stored inOEM database 28 and use the OEM data to enrich analysis of the operatingdata received from the appliances 11A-11 n in the field.

Referring to FIGS. 2-3 , each IOT device 12 comprises a device body 102configured to be supported on or nearby a refrigeration appliance. TheIOT device 12 comprises a plurality input/output (“I/O”) ports104A-104F, 106, 108, 110 configured to operatively connect the IOTdevice to essentially any type of refrigeration appliance 11A-11 n. Theports 104A-104F, 106, 108, 110 are suitably exposed on the exterior ofthe device body 102 so that a user can simply plug in one or more cableconnectors (not shown) of the refrigeration appliance, or cableconnectors of an accessory device used with the refrigeration appliance(e.g., accessory temperature monitoring devices), to operatively connectthe IOT device 12 to the refrigeration appliance. In the illustratedembodiment, the IOT device comprises six low voltage I/O ports104A-104F, a serial data port 106, an expansion port 108, and a serviceconnection port 110. As will be explained in further detail below, theports 104A-104F, 106, 108, 110 equip the IOT device 12 for severaldifferent connection modes, which enable connection of essentially anytype of refrigeration appliance 11A-11 n to the asset management system10. The multimodal connection ports 104A-104F, 106, 108, 110 and theasset-centric design of the asset management system 10 as a wholecombine to make the process of connecting any refrigeration appliance tothe asset manager 14 more user-friendly than with conventional IOTsystems.

The low voltage I/O ports 104A-104F are configured to read analog ordigital sensor or switch inputs or output low voltage switch controls.Any of the I/O ports is configured to connect to a resistive sensor,digital sensor, or low voltage switch associated with the refrigerationappliance. This facilitates remote monitoring of the sensor signals andremote control of low-voltage switches of a given appliance 11A-11 n.

The serial port 106 is operatively connected to a serial datatransceiver 114 (FIG. 3 ) contained inside the device body 102. In anexemplary embodiment, the IOT device 12 utilizes an onboard RS-485Mod-Bus transceiver 114 to communicate with an RS-485 Mod-Bus controlboard of a refrigeration appliance. Other serial data communicationprotocols could also be used without departing from the scope of thedisclosure. Because the IOT device 12 includes both low voltage I/Oports 104A-104F and a serial data I/O port 106, the IOT device isselectively configurable in various gateway modes, including (i) astandalone mode in which the refrigeration appliance is connected to lowvoltage I/O ports 104A-104F that expose sensor inputs and low voltagecontrol switches to the asset management network 20; (ii) a companionmode in which the refrigeration appliance is connected to a serial dataport 106 that, in combination with the serial data transceiver 114,exposes a digital control board of the refrigeration appliance to theasset management network; and (iii) a hybrid mode in which therefrigeration appliance is connected to a combination of one or more lowvoltage I/O ports 104A-104F and the serial data port 106 to expose both(a) direct sensor inputs and/or low voltage control switches and (b) thedigital control board of the refrigeration appliance to the assetmanagement network. In the standalone mode, the IOT device 12 connectsthe refrigeration appliance to the remote asset management system usingonly the low voltage I/O ports. In the companion mode, the IOT device 12connects the refrigeration appliance to the remote asset managementsystem using only the serial data port. And in the hybrid mode, the IOTdevice 12 connects the refrigeration appliance to the remote assetmanagement system using a combination of low voltage I/O ports and theserial data port.

In most cases, the I/O ports 104A-F, 106 are connected directly to OEMcomponents of the appliances 11A-11 n. This is in contrast toconventional refrigeration appliance monitoring solutions that arecommercially available today, which provide an IOT gateway deviceconnected to an accessory temperature sensor. For example, low voltageI/O ports 104A-104F of IOT the device 12 are configured to connectdirectly to resistive temperature sensors and low voltage switches thatare prefabricated components of the refrigeration appliance or areintegral to the refrigeration appliance's native control system. Certainrefrigeration appliances in the scope of the present disclosure (e.g.,the commercial cooler 11A, commercial freezer 11B, and/or residentialrefrigerator 11C) comprise one or more of a return air temperaturesensor located in the return air duct of the refrigeration appliance andconfigured to output a signal indicative of an air temperature in areturn air duct of the refrigeration appliance, an evaporatortemperature sensor in direct thermal communication with the evaporatorof the refrigeration appliance (e.g., at the outlet of the evaporator)and configured to output a signal indicative of refrigerant temperatureat the evaporator, a liquid line temperature sensor in direct thermalcommunication with the liquid line of the refrigeration appliance andconfigured to output a signal indicative of refrigerant temperature inthe liquid line, a condenser air temperature sensor located adjacent tothe condenser of the refrigeration and configured to output a signalindicative of an air temperature in the condenser, a door switch sensorconfigured to output a signal indicative of when the door of therefrigeration appliance is open, and/or a compressor run sensor (e.g., alow voltage relay in communication with the compressor) configured tooutput a signal indicating when the compressor is running. Any of thesesensors or switches (or other types of OEM sensors or switches) can beconnected directly to one of the I/O ports 104A-104F. The IOT device 12is configured to sample the parameter values and transmit informationabout the sampled values to the asset manager 14 via asset managementnetwork 20.

Modern ice makers 11D, 11E are typically controlled by digitalcontrollers. Ice makers 11D, 11E typically have OEM sensors and onboardcontrol logic for monitoring an even greater number of operatingparameters than other types of refrigeration appliances. In one or moreembodiments, the ice makers 11D, 11E on the asset management network areconfigured to monitor one or more of the ice level (e.g., via an icelevel sensor); one or more sensed temperatures (e.g., an airtemperature, one or more evaporator temperatures (e.g., the maximumtemperature of the refrigerant at the outlet of the evaporator during afreeze step of a previous ice batch production cycle, a temperature ofthe refrigerant at the outlet of the evaporator at a predefined point intime during a freeze step of a previous ice batch production cycle, aminimum temperature of the refrigerant at the outlet of the evaporatorduring a freeze step of a previous ice batch production cycle, a maximumtemperature of the refrigerant at the outlet of the evaporator during aharvest step of a previous ice batch production cycle), a temperature ofthe water in the sump, and/or a temperature of the supply water at thewater inlet)), one or more sensed refrigerant pressures (e.g., a sensedrefrigerant pressure on the high pressure side of the compressor (e.g.,the maximum high side pressure during a freeze step of a previous icebatch production cycle, a high side pressure at a predefined point intime during a freeze step of a previous ice batch production cycle, aminimum high side pressure during a freeze step of a previous ice batchproduction cycle, a maximum high side pressure during a harvest step ofa previous ice batch production cycle) or a sensed pressure of therefrigerant pressure on the low pressure side of the compressor (e.g.,the maximum low side pressure during a freeze step of a previous icebatch production cycle, a low side pressure at a predefined point intime during a freeze step of a previous ice batch production cycle, aminimum low side pressure during a freeze step of a previous ice batchproduction cycle, a maximum low side pressure during a harvest step of aprevious ice batch production cycle), a measured run time (e.g., amountof run time in the last day, week, and/or month), a measured water usage(e.g., amount of water consumed in the last day, week, and/or month), ameasured energy usage (e.g., amount of energy consumed in the last day,week, and/or month), a measured ice production (e.g., amount of iceproduced in the last day, week, and/or month), a measured freeze stepduration (e.g., the amount of time taken to conduct the freeze step ofthe previous completed ice batch production cycle, the amount of timetaken to conduct each of the previous predefined number of (e.g., five)freeze cycles, an average of the amount of time taken to conduct each ofthe previous predefined number of (e.g., five) freeze cycles), ameasured harvest step duration (e.g., the amount of time taken toconduct the harvest step of the previous completed ice batch productioncycle, the amount of time taken to conduct each of the previouspredefined number of (e.g., five) harvest cycles, an average of theamount of time taken to conduct each of the previous predefined numberof (e.g., five) harvest cycles). The IOT device is configured toretrieve any or all of these operating parameters from the ice makercontroller via the serial data port 108.

Other types of refrigeration appliances have varying levels of digitalcontrol capability. For the most basic refrigeration appliances, digitalcontrols are either essentially nonexistent or would not provide usefulinformation beyond what is available from direct access to sensors andswitches in the standalone gateway mode. So for these types ofrefrigeration appliances, the standalone gateway mode is appropriate.But for other refrigeration appliances with some level of digitalcontrol capability, where additional information beyond what can beobserved through the low voltage sensors and switches accessed in astandalone mode is available by connection to the digital control board,the hybrid gateway mode is preferred.

The expansion port 108 is configured to facilitate additionalconnections of appliance-related sensors, controllers, and/or actuatorsto the asset management system 10. For example, refrigeration appliancesused in scientific applications may be used in combination withprecision-calibrated auxiliary sensors that monitor the appliance and/orproduct contained therein. Also, for refrigeration appliances withmultiple independent refrigeration circuits, the expansion port can beused to multiply the port connectivity of the IOT device 12. Theexpansion port 108 makes the IOT device 12 highly configurable to acceptconnections to virtually any I/O device related to the refrigerationappliance, accessory devices used in combination with the refrigerationappliance, and/or product related sensors or actuators. Further, theexpansion port 108 could also be used to enable connections to othertypes of kitchen appliances besides refrigeration appliances, furtheraugmenting the capabilities of the asset management system 10.

In the illustrated embodiment, the service port 110 comprises a USB-Cport configured to facilitate connection of the IOT device 12 to aservice computing device (e.g., a laptop computer or mobile device; notshown). The service port 110 allows a service technician to connect tothe refrigeration appliance through the IOT device 12, gaining wiredaccess to the appliance. In some embodiments, the IOT device 12 isconfigured so that a service technician can connect a service computingdevice to the service port 110 and run a service routine from theservice computing device that causes the appliance to perform adiagnostic or maintenance operation.

In the illustrated embodiment, the IOT device body also includes a powerport 116 for connecting the IOT device 12 to main electrical power MP(e.g., AC power). The IOT device 12 further comprises an onboard backuppower source 120 (FIG. A2) configured to power certain IOT devicefunctions when there is a main power outage. In the illustratedembodiment, the onboard backup power source 120 is a rechargeable backuppower source. Accordingly, as shown in FIG. 3 , the IOT device 120comprises a charging circuit 122 for charging the backup power source120. In one or more embodiments, the backup power source 120 comprises alithium capacitor. In certain embodiments, the backup power source 120is a battery, such as a lithium ion battery, a solid state battery, orany other suitable type of battery. Although a rechargeable backup powersource is shown, it is contemplated that the backup power source couldbe non-rechargeable, e.g., a non-rechargeable battery rated for a longlifespan, such as a ten-year lifespan.

The IOT device 12 further comprises an edge computing device 130(broadly, an IOT device controller or host device). The edge computingdevice 130 is configured to control the IOT device 12 and performcertain edge computing operations in the remote asset management network20. The edge computing device 130 generally comprises a processor and amemory storing processor-executable instructions that, when executed bythe processor, configure the processor to control the IOT device toperform IOT device control functions and/or edge computing functions.

As explained more fully below, when the IOT device 12 is operativelyconnected to the asset management system 10, the asset manager 14uploads a refrigeration appliance profile (broadly, an applianceprofile) to memory of the edge computing device 130. The applianceprofile configures the IOT device 12 for connection to its specificappliance based on known characteristics of the appliance type. Forexample, the appliance profile can configure the processor of the edgecomputing device 130 for reading operating data from respective ones ofthe low voltage I/O ports 104A-104F and/or serial data port 106 to whichone or more low voltage cables and/or a serial data cable of arefrigeration appliance are connected. Thus, in one aspect, theappliance profile can provide instructions for how the IOT device 12reads and/or writes information to/from the I/O ports 104A-104F, 106. Inother words, the appliance profile provides a definition, based on theappliance type, of which types of sensors, low voltage switches, and/orserial control boards are connected to the IOT device 12. In anotheraspect, the appliance profile can define one or more alarm thresholdsand configure the processor of the edge computing device 130 to outputan alarm indication when the operating data read from the respectiveones of the low voltage I/O ports 104A-104F and/or serial data port 106crosses the one or more alarm threshold. The appliance profile can thusdefine alarm set points for the appliance based on the appliance type.As explained more fully below, the asset management system'sasset-centric architecture allows for seamlessly applying the correctappliance profile when the IOT device 12 is bound to the appliance.

In an exemplary embodiment, the appliance profile has two parts: anoperating data profile part and an alarm profile part. If the expansionport 108 of the IOT device 12 is used, the appliance profile can includestill more parts to account for the expansion port connections. Theoperating data profile part defines the operating data that the edgecomputing device 130 should read from or write to each port 104A-104F,106. An example operating data profile part for a standalone-typerefrigeration appliance is shown in Table 1 below:

TABLE 1 Input Connection 104A Return Air Temp 104B Evap Temp 104C LiquidLine Temp 104D Ambient Temp 104E Door Switch 104F Compressor Run

The operating data profile part shown in Table 1 is for certain types ofrefrigeration appliances to which the IOT device 12 connects via onlythe low voltage I/O ports (e.g., in standalone mode). When the serialdata port 106 is used, the operating data profile part configures theedge computing device 130 to read/write from/to the serial datacontroller of the appliance in a Mod-Bus format. For example, theoperating data profile part can define an environmental variableconfiguration profile in the following format:

{  SWITCH_DUTY_CYCLE_SECONDS:    0, 0, 0, 0, 3600, 86400; TEMP_ALERT_REPEAT_SECONDS:   216000, 216000, 216000, 216000, 216000,216000;  TEMP_ALERT_SECONDS:  7200, 0, 3600, 0, 600, 43200; TEMP_EXCEPTION_HIGH: 8, 120, 50, 120, 120, 120;  TEMP_EXCEPTION_LOW: 2,−60, −60, −60, −60, −60; }And a Mod-Bus configuration variable profile in the following format:

{  CONTROL_MODBUS_FAST:R0,0,0.1;R1,1,0.1;R2,2,0.1;R203,203,0.1;R707,707,1; CONTROL_MODBUS_PARAM: R198,198,1;R199,199,1;  CONTROL_MODBUS_CHANGE:alm128,128,1;alm129,129,1;alm130,130,1;alm131,131,1;  alm132,132,1;alm135,135,1;  CONTROL_MODBUS_FILES:FAST,3600;PARAM,86400;CHANGE,60; }

The alarm profile part configures the edge computing device 130 fordetecting certain alarms on the edge of the IOT network 20. An exemplaryalarm profile part is shown below in Table 2:

TABLE 2 Type Level Category Description Criteria Delay Probe PowerCritical Power A main power failure — — — Failure Alarm has beendetected. Door Critical Product Door has been left — 15 min S5 OpenAlarm ajar. High Critical Product Cabinet temperature is 44 F. 1 hour T1Temp Alarm too high. Low Critical Product Cabinet temperature is 32 F. 1hour T1 Temp Alarm too low. High Warn Asset The compressor run — 6 hoursS6 Run time limit has been Time exceeded. High Warn Asset High condenser120 F.  1 hour T3 Cond. temperature detected. Temp

The IOT device 12 is configured for transmitting refrigeration applianceoperating data to the network 18 for remote monitoring by the assetmanager 14. More particularly, the edge computing device 130 isconfigured to (i) control the sampling or reading of parameters from theI/O ports 104A-104B, 106 at a defined sampling frequency; (ii) aggregateor form the sampled data into data packets; and (iii) transmit the datapackets onto the asset management network 20 at a transmissionfrequency. In one or more embodiments, the IOT device 12 has a defaultsampling frequency of one sample-per-second (e.g., the samplingfrequency can be in an inclusive range of 0.1 to 10 samples-per-second)and a default transmission frequency of one data packet every 30 minutes(e.g., the default transmission frequency is in an inclusive range offrom 1 data packets-per-day to 120 data packets per hour). Accordingly,by default, the sampling frequency is much greater than the transmissionfrequency. Suitably, the default sampling and transmission frequenciesare adjustable to suit application-specific needs.

Independent and asynchronously of the default transmission frequency,the edge computing device 130 is configured to perform an edge computealarm operation whereby the edge computing device immediately transmitsan alarm notification to the asset management network 20 when a sampledoperating parameter of the refrigeration appliance crosses an alarmthreshold (e.g., an alarm threshold set by the appliance profile). Thatis, the memory of the edge computing device 130 storesprocessor-executable edge alarm instructions that configure theprocessor to determine when the sampled operating data crosses an alarmthreshold and immediately transmit an alarm indication to the remoteasset management system. The IOT device 12 processes the alarmconditions on the edge of the asset management network 20 so that it cantransmit alarm indications asynchronously with respect to thetransmission frequency for normal transmission of aggregated operatingdata (data packets). This enables the IOT device 12 to notify the assetmanager 12 of an alarm event in real time, instead of waiting to reporton the alarm event until the subsequent operating data transmission(data packet). As a result, the asset management system 10 is capable ofgenerating truly actionable push notification alarms in real time toalert appliance users of any critical issue that risks damage to theappliance or loss of product.

In addition to controlling the transmission of appliance operating dataonto the asset management network 20, the edge computing device 130 isfurther configured to receive control instructions from the assetmanager 14 via the asset management network 20. Based on the applianceprofile, the edge computing device 130 is configured to facilitatecontrol of the appliance in accordance with the instructions. Forexample, when the appliance has a digital control board connected to theserial data port 106, the edge computing device 130 writes the controlinstructions to the appliance control board via the serial data port106. When the appliance lacks a digital control board but has one ormore low voltage switches connected to a low voltage I/O port 104A-104F,the edge computing device 130 outputs low voltage control signals to theswitches in response to control instructions from the asset manager 14.

In the illustrated embodiment, the IOT device comprises a hardwaresecurity chip 132 that stores encryption keys. The hardware securitychip 132 is broadly configured to encrypt data packets transmitted tothe asset management network 20 and decrypt data transmissions from theasset management network. The hardware security chip 132 functions tofacilitate encryption/decryption with the data broker 16 without anexchange of keys. In an exemplary embodiment, the hardware security chipis an STSAFE-A100 device, available from STMicroelectronics.

In an exemplary embodiment, the edge computing device 130 is configuredfor performing additional asset management system functions “on theedge” of the asset management network 20. One example of an edge computefunction is the edge compute alarm operation described above. Theinventor believes that this edge computing capability makes the pushnotification alarms generated by the asset management system 10 muchmore responsive than push notification alarms generated by conventionalremote refrigeration appliance monitoring systems. Additional edgecompute functions besides the edge compute alarm operation are alsocontemplated.

For example, the edge computing device 130 can be configured to analyzethe appliance operating data on the edge of the asset managementnetwork. This is useful because it provides a mechanism for conductingtime-sensitive analyses on a sample-by-sample basis rather than on adata-transmission-by-data-transmission basis in the asset manager 14.One example of such a time-sensitive analysis is a product simulationanalysis described in further detail below. That is, the edge computingdevice 130 is configured to periodically run a simulation of a producttemperature (e.g., on a sample-by-sample bases) based on return airtemperature read from one of the I/O ports 104A-104F, 106. In certainembodiments, the edge computing functions are programmed to the edgecomputing device 130 by the asset manager 14. For example, default edgecomputing functions according to appliance type can be programmed whenthe asset manager 14 uploads the appliance profile to memory of the edgecomputing device 130 at the time the IOT device 12 is connected to thenetwork 20. Furthermore, the asset manager 14 can be configured to allowa user to adjust the edge computing functions performed by one or moreIOT devices 12.

Another example of an edge computing operation performed by the edgecomputing device 130 on the edge of the asset management network 20 isbatch aggregation operations. As mentioned above the edge computingdevice 130 is configured to sample the operating parameters at samplingfrequency that is much greater than the transmission frequency. In otherwords, the transmission frequency is less than the sampling frequency.Hence, each data packet transmission will aggregate data from aplurality of samples or readings of the operating data. The edgecomputing device 130 is configured to perform batching operations on thesampled data before each data packet is transmitted to the assetmanagement network. For example, in one or more embodiments, for eachoperating parameter being monitored, the edge computing device isconfigured to calculate the average parameter value for the reportinginterval, the minimum sampled value during the reporting interval, themaximum value for the reporting interval, and/or a filtered parametervalue that is a function of an exponential decay value. The edgecomputing device 130 includes these batch parameter values in the datapackets transmitted to the asset management network 20.

The memory of the edge computing device 130 also stores power managementinstructions that configure the processor to conduct a power managementroutine 150, shown in FIG. 4 . The starting point 152 for the powermanagement routine 150 is when the IOT device 12 is bound to anappliance and turned on while main power MP is available. At step 154,the IOT device 12 draws power from the main power supply MP to performall device functions, e.g., periodically sampling operating data fromthe refrigeration appliance, transmitting the operating data to theasset management network 20 via the modem 140, edge alarm monitoring andnotification, etc. As indicated at decision point 156, whenever mainpower MP is available, the IOT device 12 continuously draws main powerMP and performs device functions normally. For example, during normaloperation when main power is sustained at the main power port, the edgecomputing device 130 samples the operating data at a normal samplingfrequency and transmits the operating data to the asset managementnetwork 20 via the modem 140 at a transmission frequency less than thenormal sampling frequency.

When a main power outage occurs, at decision point 156, the edgecomputing device 130 determines that main power is lost, e.g., byunavailability at the power port 116. In response to recognizing loss ofpower from the main power supply MP, the IOT device 12 draws power fromthe backup power supply 120 to run a power failure routine 160. Onset ofthe power failure routine 160 begins tolling a timer for a low powertime interval (step 161). Also, at step 162, the edge computing deviceimmediately transmits a power outage alarm notification to the assetmanagement network 20 via the modem 140. In one or more embodiments,this is the only transmission to occur during the power failure routine160. Subsequently, at step 163, the edge computing device samplesoperating data from the refrigeration appliance without transmitting thesampled operating data to the asset management network 20. During thelow power time interval, the edge computing device 130 is configured tosample the operating data at a low power sampling frequency. In one ormore embodiments, the low power sampling frequency can be less than thenormal sampling frequency. Alternatively, the low power samplingfrequency can be the same as the normal sampling frequency or greaterthan the normal sampling frequency.

At decision point 164, the edge computing device determines whether mainpower MP has been restored (e.g., based on availability of main power atthe power port 116). If main power MP has not been restored, at decisionpoint 165, the edge computing device 130 determines whether the lowpower time interval has elapsed. If not, sampling (step 163) continues.Throughout the low power sampling interval, the edge computing device130 continues monitoring for main power restoration and sampling theoperating data. If main power MP is restored at any time during the lowpower sampling interval, the edge computing device 130 advances to step170 and transmits one or more data packets containing the data sampledduring the low power sampling interval. If (at decision point 165) thelow power time interval elapses before main power MP is restored, theedge computing device 130 switches the IOT device to a sleep mode (step166). In the sleep mode, the edge computing device 130 ceases samplingthe operating data from the refrigeration appliance. Essentially theonly function performed by the IOT device during sleep mode ismonitoring for main power restoration (decision point 167). When mainpower MP is restored after sleep mode, the IOT device is taken out ofsleep mode to automatically transmit one or more data packets containingthe sampled operating data from the low power time interval.

Accordingly, it can be seen that the power management routine 150responds to a main power outage by immediately transmitting an alarm tothe asset manager 14 and then making a further two-stage response.Initially, during the low power time interval, the processor samplesoperating data but does not transmit the operating data to the assetmanagement network 20. This preserves power from the backup power source120 while maintaining data integrity throughout the low power timeinterval. All data collected during the low power interval isautomatically reported to the asset manager 14 when main power isrestored. The second stage of the power failure routine 160 is thesubsequent sleep mode stage. During this stage, the IOT device 12 ceasesmonitoring the operating data altogether. This provides maximum powersavings to preserve the life of the backup power supply 120. Thistwo-stage routine may be particularly useful for backup power suppliesthat are non-rechargeable.

The duration of the low power time interval is set so that, after a mainpower outage, operating data such as air temperature inside therefrigeration appliance are continuously monitored for at least as longas product inside the refrigeration appliance may not be spoiled by thepower loss. The power failure routine 160 assumes that main power lossat the IOT device 12 occurs when the refrigeration appliance isunpowered and cannot provide refrigeration to any product containedtherein. As a practical matter, in room temperature ambient conditions,any cooler or freezer can only maintain product at a sufficiently lowtemperature to stave off spoilage for a limited time. After that timeperiod elapses, the product will be spoiled and must be discarded. Inthe inventor's view, further monitoring of the refrigeration applianceis wasted (and wastes backup power) when the appliance cannot draw powerand the product inside is already spoiled. Accordingly, the low powertime interval is set to a duration corresponding to how long productinside the refrigeration appliance can plausibly maintain temperaturewithout spoiling when powered refrigeration is unavailable. In one ormore embodiments, the low power time interval is in an inclusive rangeof from 4 hours to 24 hours. After the low power time interval elapses,the processor is configured to put the IOT device 12 into a sleep modein which no sampling is conducted. When the power failure routine isexecuted by the processor, whenever the main power is restored, theprocessor is configured to transmit one or more data packets containingthe operating data collected during the low power time interval. Hence,even when refrigeration appliances lose power, the asset managementsystem 10 is configured to maintain a continuous record of the operatingdata for the entire time that product may remain unspoiled.

Referring to FIG. 3 , the IOT device 12 further comprises a networkmodem 140 (broadly, a network port) disposed inside the device body 102.The edge computing device 130 is connected to the network modem 140 anduses the network modem to communicate with the asset management network20. The modem 140 is configured to transmit operating data to the assetmanager 14 and receive control instructions from the asset manager viathe network 20. In the illustrated embodiment, the modem 140 is awireless modem. But in other embodiments, the modem can be configuredfor wired network connection, e.g., the modem could comprise an Ethernetmodem connected to an Ethernet receptacle.

In an exemplary embodiment, the modem 140 is a cellular modem and thenetwork 18 to which the modem wirelessly connects is an off-the-internetcellular subnet. The device ID of the cellular modem 140 is the IMEI.Those skilled in the art will appreciate that utilization of anoff-the-internet cellular subnet provides enhanced data security byeliminating attack vectors from the open internet. In a preferredembodiment, the modem 140 is a Blues Wireless Notecard. The BluesWireless Notecard 140 is a low power cellular device-to-cloud data pumppreloaded with 500 MB of data and ten years of service. The IOT device12 is configured to transmit data packets that are very compact in termsof data utilization. As a result, the 500 MB of preloaded data providedby a Blues Wireless Notecard can be sufficient for all data packetstransmitted over a 10-year lifespan of an IOT device 12. Blues WirelessNotecards include an embedded SIM that is configured so that the IOTdevice 12 will automatically connect to the sub-net 18 when turned on.Accordingly, in one or more embodiments, an IOT device 12 in the scopeof the present disclosure comprises a cellular modem 140 such as anLTE-M modem, an NB-IoT modem, etc.

The inventor currently prefers cellular modems for deploying IOT inrefrigeration appliances. For the purpose of creating useful assetmanagement systems for refrigeration appliances, cellular networks haveadvantages over other network communication standards such as wiredEthernet, Wi-Fi, and long-range radio. In particular, because cellularnetwork infrastructure is wireless, is standardized, has wideavailability in virtually every populated location, and is robustlymaintained by cellular network providers, IOT solutions can be widelydeployed on cellular networks with little to no network configuration ormaintenance by the owner or operator of the refrigeration appliance. Bycomparison, deployment of IOT solutions on wired Ethernet, Wi-Fi, andlong-range radio networks require more end user effort. In the case ofwired Ethernet, an end user must make a wired connection to the device,which may not even be feasible in all cases. In the case of Wi-Fi, anend user must configure the initial connection and ensure it ismaintained. Even then, losses of Wi-Fi connectivity are much morefrequent than cellular. Long-range radio networks such as LoRaWANnetworks are not as ubiquitous as cellular networks, so currently theuse of long-range radio requires setting up underlying networkinfrastructure.

In the future, it is conceivable that long-range radio standards couldbecome more widely adopted. And at that point, long-range radio maybecome a desirable alternative to cellular for deploying IOT inrefrigeration appliances. Accordingly, it is expressly contemplated thatthe IOT devices, systems, and networks, which the present disclosuredescribes in relation to cellular network protocols, could alternativelybe configured for communication using a standardized long-range radiocommunication protocol such as LoRa. Those skilled in the art willrecognize that aspects of this disclosure pertaining to asset-centriccommunication can be adapted to IOT devices using LoRa modems.

Referring to FIG. 2 , each IOT device 12 is prefabricated emblazonedwith a unique QR code 250 (broadly, a machine-readable code) encoding aweb address including the device ID of the modem. The QR code may beemblazoned by applying a QR code sticker in the location of referencenumber 250 in FIG. 2 . The QR code 250 stores a URL that accesses apublic webpage for linking the IOT device 12 to the respectiverefrigeration appliance 11A-11 n. Once the link between the IOT device12 and the respective refrigeration appliance 11A-11 n is established,the URL contained in the QR code resolves to a public page containingoperating data for the refrigeration appliance.

Referring again to FIG. 1 , the illustrated asset management network 20comprises the cloud-based data broker 16 between the asset manager 14and the IOT devices 12. In general, the data broker 16 is a softwareapplication stored in remote memory and executing on a remote processor(e.g., the processor of a server or cloud service). The data broker 16is broadly configured to connect the IOT devices 12 to the asset manager14. More particularly, the data broker 16 connects the IOT devices 12 tothe asset manager by receiving data packets from the IOT devices,determining the serial number of the source appliances for the datapackets, creating structured data objects for the data packets, taggingthe structured data packets by serial number of the source refrigerationappliances, and transmitting the tagged structured data packets to theasset manager 14.

In one or more embodiments, the data broker 16 is also configured tofacilitate binding the IOT devices 12 to refrigeration appliances. Asexplained more fully below, the data broker 16 is configured to receiveweb page requests from one or more client devices that enter a webaddress pointing the data broker and including the device ID(specifically, the web address encoded in the QR code 250 emblazoned onthe IOT device 12). In response to such a web page request, the databroker 16 is configured to determine based on the included device IDwhether the IOT device has been bound to a refrigeration appliance. Andin response to determining based on the included device ID that the IOTdevice 12 has not been bound to a refrigeration appliance, the databroker 16 is further configured redirect the web page request to anotherweb address for a web form that facilitates binding the IOT device tothe refrigeration appliance by inputting a serial number for theappliance. Upon input of the serial number, the data broker 16 binds theIOT device 12 to the refrigeration appliance and therefore transmits alldata received from the IOT device (tagged with the IMEI for the modem)as structured data objects tagged by the serial number of the appliance.If the web address encoded in the QR code 250 of an IOT device 12 thatis already bound to a refrigeration appliance is entered into a browserof a client device (e.g., by scanning the QR code 250), the data broker16 again receives the web page request. But in this instance, the databroker 16 determines that the IOT device 12 is already bound to anappliance and redirects the client device to another web address for apublic web page for displaying operating data for the respectiverefrigeration appliance.

In an exemplary embodiment, the data broker 16 is the notehub.io cloudservice, available from Blues Wireless. Notehub.io is a cloud serviceapplication for securely routing data from Blues Wireless notecarddevices to third party cloud applications, in this case, the assetmanager 14. In the illustrated embodiment, the notehub.io service isused to define data routes to the cloud-based asset manager application14. In addition, the notehub.io data broker service 16 is configured tostructure data object payloads that are sent to the asset manager 14.

Various data object structures could be used without departing from thescope of the disclosure. In general, each structured data object shouldbe tagged by serial number of the source refrigeration appliance. In oneor more embodiments, the data broker 16 is configured The notehub.ioservice has native JSON transformation capabilities (JSONata). So in anexemplary embodiment, the data broker 16 is configured to structure thedata packets received from the IOT devices 12 as JSON data objects. Anexemplary JSON object structure is shown below:

{  “device”: “dev:868050045502792”,  “sn”: “10669845”,  “received”:1690319105.739838,  “when”: 1690319103,  “body”: {   “s5”: {   “closed”: true,   },   “s6”: {    “closed”: true,   },   “seconds”:300,   “t1”: {    “avg”: −19.796875,    “cur”: −17.6875,    “filt”:−18.015625,    “max”: −17.6875,    “min”: −21.8125   },   “t2”: {   “avg”: −19.125,    “cur”: −17.8125,    “filt”: −17.96875,    “max”:−17.78125,    “min”: −24.390625   },   “t3”: {    “avg”: 24.859375,   “cur”: 23.28125,    “filt”: 23.203125,    “max”: 30.34375,    “min”:23.015625   },   “t4”: {    “avg”: 25.390625,    “cur”: 25.921875,   “filt”: 25.921875,    “max”: 25.984375,    “min”: 24.171875   }  } }

In this example, “device” is the modem ID for the IOT device 12 thatsent the data packet; “sn” is the serial number of the sourcerefrigeration appliance, “received” and “when” define the times the datawas transmitted, and “body” contains the operating data for therefrigeration appliance. The above JSON data structure is for arefrigeration appliance and IOT device operating in the standalone mode.Hence, there are six operating parameters, “s5,” “s6,” “t1,” “t2,” “t3,”and “t4.” The parameter “s5” represents the door switch of therefrigeration appliance, in this case showing that the door switch isclosed. The parameter “s6” represents the compressor relay, in this caseshowing that the compressor relay is closed, meaning the compressor isoff. In this case, the parameters “t1,” “t2,” “t3,” and “t4”respectively represent the return air temperature, evaporatortemperature, liquid line temperature, and condenser air temperature. Asshown, the JSON data object includes aggregated values for thesetemperatures during a 300-second sampling interval. For each temperatureparameter, the JSON data object includes the average temperature duringthe interval (“avg”), most recent sampled temperature (“cur”), value ofan exponential decay filter value for the sampling interval (“filt”),the maximum value read during the sampling interval (“max”), and theminimum value read during the sampling interval (“min”). Again, theseaggregated or batch values for each data object are calculated by theedge computing device 130 of the IOT device 12 and simply reformattedinto the JSON data object structure by the data broker 16.

Based on the JSON object above, it can be seen that the asset managementsystem 10 is configured to communicate refrigeration appliance operatingdata to the asset manager 14 as structured data objects, where each dataobject includes a key-value pair defining the serial number (e.g., “sn”:“10669845”) of the refrigeration appliance that generated the operatingdata. While the illustrated asset management system 10 utilizes thenotehub.io data broker service 16 to structure the data objectsremotely, it will be understood that asset management systems inaccordance with the present disclosure could have other networkarchitectures, while still utilizing the principles of the data objectstructure described above. For example, instead of configuring the assetmanagement network 20 to have an intermediate data broker layer, thedata broker layer could be omitted. In this type of networkarchitecture, the device layer would be responsible for structuring thedata objects. In other words, the edge computer 130 of each IOT device12 could be configured for structuring the data in each data packet intoa JSON data object (or other suitable structured data object) thatincludes a key-value pair defining the serial number of therefrigeration appliance. An advantage of the illustrated networkarchitecture is that the notehub.io data broker service 14 uses itsnative HTTPs encryption systems for data security. This reduces thecomputational requirements for the edge computing devices 130.

Regardless of how the network architecture is constructed, tagging thedata objects with the serial number of the refrigeration applianceenables the asset manager 14 to seamlessly store refrigeration applianceoperating data in a time series database with the refrigerationappliance serial number as the primary key. Referring to FIG. 5 , thebackend components of the asset manager 14 are shown schematically atreference number 200. In general, the asset manager backend 200 isconfigured to stream the structured data objects from the data broker16, parse the streamed structured data objects by serial number of thesource refrigeration appliance, and store the operating data containedin the structured data objects in a time series database using theserial numbers of the source refrigeration appliances as primary keys.The backend 200 is further configured to expose the stored data to thefront end applications 22, 24. The components shown schematically inFIG. 5 represent various software modules and databases used by thebackend 200.

The backend 200 comprises an extract transfer load (ETL) system 202configured to stream incoming data from the asset management network 20,transform it into the appropriate format, and load it into a time seriesdatabase 204. Recall that the asset management system 10 may bemassively scaled and distributed. Accordingly, the ETL system 202 shouldbe capable of streaming a massive amount of incoming data from a massivenumber of refrigeration appliances 11 n. Hence, on a massive scale, theETL system 202 is configured to stream structured data objects from theasset management network and parse the structured data objects inreal-time to determine, for each structured data object, the key-valuepair for the serial number of the refrigeration appliance and therespective operating data.

In the illustrated embodiment the ETL system 202 receives structureddata objects from the notehub.io data broker 16. The data objects arecontinuously being streamed to the asset manager 14 from all of the IOTdevices 12 in the asset management network 20. To manage the large datastream and its many sources, the ETL system 202 comprises a dataextraction module 206, a data streaming module 208, and a data ingestionmodule 210. The illustrated data extraction module 206 comprises anHTTPs module that authenticates and decrypts the data transferred fromthe upstream network 20. The illustrated data streaming module 208 is anApache Kafka streaming module. The streaming module 208 processes theincoming data from the asset management network 20 in real time. In oneaspect, the streaming module 208 transforms the incoming data forstorage in the time series database 204. For example, the streamingmodule 208 parses each individual structured data object in real time todetermine the serial number of the refrigeration appliance thatgenerated the operating data based on the key-value pair defined in theJSON object. In an exemplary embodiment, the streaming module 208 parsesthe structured data objects according to parsing logic agnostic to modemdevice ID. After the data is processed and transformed by the Kafkamodule 208, the data ingestion layer 210 loads the data into the timeseries database 204.

In the illustrated embodiment the time series database 204 is a MongoDBdatabase. The time series database 204 uses the serial numbers of therefrigeration appliances 11A-11 n as primary keys. In an exemplaryembodiment, the time series database 204 is agnostic to the modem deviceIDs of the IOT devices. In the illustrated embodiment, the asset managerbackend 200 further comprises a batch aggregation component 212configured to perform one or more batch aggregation operations on thetime series data loaded into the time series database 212. For example,the batch aggregation component 212 reads the time series data from thedatabase 202 and periodically generates summary data (e.g., movingaverages) based on the underlying time series data.

The Kafka streaming module 208 is further configured to enableevent-driven processing. In this application, the Kafka streaming module208 and the edge computing devices 130 cooperate to generate trulyactionable push notification alarms for the user. As explained above,the edge computing devices 130 of the IOT devices 12 are configured totransmit unscheduled alarm notifications when predefined alarmthresholds are crossed. The Kafka streaming module 208 is configured torecognize unscheduled alarm notifications as an event and immediatelytrigger a push notification alarm routine. The illustrated asset managerbackend system 200 comprises an alarm service module 214 incommunication with a notification service module 216. When the Kafkastreaming module 208 detects an unscheduled alarm notification event,the ETL module 202 loads the alarm notification to the alarm servicemodule 214. The alarm service module then executes rules-based logic todetermine whether to send the alarm to the notification service. Forexample, using a front end application 24, 26, a user can configure thealarm service module 214 to delay sending an alarm notification to thenotification service module 216 until a particular alarm state has beenmaintained for a user-defined time interval. Likewise, using a front endapplication, a user can configure the alarm service module 214 toescalate certain alarm indications. In other words, the asset manager 14interfaces with a front end application configured to facilitate useradjustment of the rules-based logic. The alarm service module 214 isconnected to a cache 217 (e.g., a Redis cache), which provides memoryfor the alarm service. For example, the cache 217 stores past alarmindications for reference when executing the alarm service logic.

The push notification service module 216 is configured to receive thealarm indications sent by the alarm service module 216 and executerules-based push notification logic to push notifications of the alarmto appropriate users via SMS message or email. For example, therules-based logic defines who is to receive push notifications for eachtype of alarm. Furthermore, a user of multiple refrigeration appliancescan assign each appliance to a respective appliance location and setpush notification rules by location. For instance, the push notificationrules can be configured so that, in response to multiple alarms causedby a power outage at a single location or store where a plurality ofrefrigeration appliances are located, the push notification service 216only pushes a single alarm notification for the location/store—not onepush notification alarm for each of the plurality of appliances at thelocation/store. In one or more embodiments, the asset manager 14interfaces with a front end application 22, 24 to facilitate useradjustment of the rules-based logic for the push notification servicemodule 216.

Accordingly, it can be seen that the illustrated asset management system10 has a multi-layer push notification architecture that enables thesystem to push actionable alarm notifications to the relevant users in atimely fashion, enabling the user to take corrective action beforeproduct is lost or the refrigeration appliance is damaged. The firstlayer of the push notification architecture is the device layer,specifically the edge computing devices 130. The device layer isresponsible for flagging any alarm condition that occurs at theappliance. The basic alarm conditions that cause an edge computingdevice 130 to flag an alarm are user configurable via front endapplications 22, 24. Every edge computing device 130 in the device layersamples the relevant parameters from the respective refrigerationappliance on a very frequent basis, on the order of once-per-second.Accordingly, when any alarm condition occurs, within seconds, the IOTdevice 12 reports the alarm condition to the asset management network asan event. The device layer works in conjunction with the ETL system 202to ensure that each alarm is timely analyzed by the alarm service module214. That is, the ETL layer is responsible for recognizing the alarmnotifications in the incoming stream of appliance data and immediatelysending each alarm to the alarm service module 214. The alarm servicemodule 214 is another layer responsible for enacting user-defined logicdeterminative of when to push a notification for an alarm. Inconjunction with the cache 217, the alarm service module 214 followsuser-defined rules to delay or escalate the alarms received from the ETLlayer 202. The alarm service module 214 is configured to notify the pushnotification service module 216 when push notification is required foran alarm indication. Any alarms that satisfy the user-defined rules fornotification are then processed by the notification service module 216,which acts as a final layer in the alarm network architecture, to applyuser-defined rules for who should receive the alarm. The pushnotification service module 216 is configured to execute rules-basedlogic to determine one or more notification addresses to receive pushnotification about the alarm condition and subsequently push thenotification to the one or more notification addresses. Hence, thenotification layer 216 functions to ensure that the appropriate SMS textmessages and emails are pushed to the appropriate users. The multi-layerpush notification alarm system described herein is able to push an alarmnotification to any user by SMS text message or email in accordance withthe user's preferred rules within 60 seconds of the triggering any alarmevent occurring at any refrigeration appliance. Those skilled in the artwill appreciate that conventional asset management systems forrefrigeration appliances cannot ensure all alarms are pushed to the userin such an actionable timeframe.

The asset manager backend 14 further comprises a meta informationdatabase 230. The meta information database 230 stores meta informationabout the refrigeration appliances 11A-11 n. For example, the database230 may comprise a relational database (e.g., an SQL database) storing,for each appliance 11A-11 n, meta information about the appliance suchas the appliance location, customer, store where the appliance islocated, refrigeration appliance type (e.g., model number), etc. As withthe time series database 204, the meta information database 230 canstore meta information using the appliance serial number as the primarykey. In one or more embodiments, the push notification service module216 is configured to reference the meta information for the respectiverefrigeration appliance and execute the rules-based logic based on themeta information to determine push notification addresses. For example,the meta information can include information about types of users foreach refrigeration appliance, and the push notification service module216 can be configured to reference the meta information database 230 toexecute rules-based logic defining which types of the users are toreceive push notification based on a type of alarm condition. Likewise,the meta information can include information about a store where eachrefrigeration appliance is deployed, and the push notification servicemodule 216 can be configured to reference the meta information database230 to execute rules-based logic defining a number of push notificationsto send to a push notification address when alarm indications arereceived from a plurality of refrigeration appliances deployed at a samestore.

The meta information database communicates with a cold storage system232 and an identity access management system 234. The identity accessmanagement system 234 provides user authentication and access controlfor the front end applications 22, 24.

As can be seen, the asset manager backend 200 further comprises anapplication programming interface API 240 providing an interface betweenthe backend and external systems like the front end web application 22,the front end mobile application 24, the OEM database 28, and theauxiliary fleet manager 26. The API 240 is configured to provide aninterface for the front end web application 22 and the front end mobileapplication 24 to access the data stored in the time series database204, the meta information database 230, and the alarm service 214. Thefront end applications 22, 24 use access to this information to providedetailed information about the operating condition of the appliances11A-11 n to the appropriate users. The API 240 also provides aninterface for the auxiliary fleet manager 26 to access the time seriesdata and meta information about the appliances 11A-11 n in therespective appliance fleet. This enables the auxiliary fleet manager 26to load appliance operating data into the fleet manager's proprietarysystems for analyzing and acting on the data. Lastly, the illustratedAPI 240 provides an interface to the OEM database 28. As will beexplained in further detail below, the backend is configured to executelambda functions 242 that combine the proprietary OEM data stored in theOEM database 28 with the appliance information contained in the backend200 to provide improved analysis of the appliance operating data.

In one or more embodiments, the OEM database 28 contains proprietary OEMdata organized by refrigeration appliance type. One example, ofproprietary OEM data is regulatory testing data by appliance type.Regulatory testing data is one example of empirically derived data forrefrigeration appliances. For example, OEMs of refrigeration appliancesconduct regulatory testing of each refrigeration appliance model thatthey manufacture. The results of the energy testing for each appliancecan be stored in the OEM database 28 and used to deriver bespoke alarmprofiles for refrigeration appliances by refrigeration appliance type(e.g., by model number). In general, the OEM database 28 can store anytype of empirically derived data for refrigeration appliances,preferably organized by appliance type.

Refrigeration appliance OEMs conduct regulatory tests for each appliancetype. During certain regulatory tests, the refrigeration appliance isrun under specified conditions while operating data is collected fromthe refrigeration appliance and product simulators (i.e., temperatureprobes potted in a product simulation vessel) are placed at a pluralityof spaced apart locations throughout the refrigeration appliance. Table3 below shows partial exemplary data for a roughly 20-minute intervalduring a regulatory test of a refrigeration appliance. In the table,Temp. Ret. represents the difference between the measured return airtemperature and the set point. Temp. Int. is the difference between themeasured supply temperature and the set point. SIM1-SIM10 each representthe difference between the temperature of a simulation probe and the setpoint for one of 13 product simulators placed at strategic locationsthroughout the refrigeration appliance. Suction is the temperature ofthe suction line of the refrigeration appliance, and Liquid is thetemperature of the liquid line of the refrigeration appliance. Ambientand RH % are respectively the ambient temperature and ambient relativehumidity in the test environment. % Run is the duty cycle of thecompressor during the test interval, and Cycles are the numbercompressor run cycles that have been conducted during the test.

TABLE 3 Temp. Temp Time Ret. Int. SIM1 SIM2 SIM3 SIM4 SIM5 SIM6 SIM7SIM8 SIM9 SIM10 5 2.8 3.4 −2.8 −4.1 1.2 −0.1 −2.8 −4.1 0.4 −0.1 −2.4−3.3 6 −1.2 −1.2 −2.8 −4.1 1.2 −0.1 −2.8 −4 0.5 −0.1 −2.5 −3.2 7 −2.1−3.1 −2.8 −4.1 1.2 −0.1 −2.8 −4.1 0.5 −0.1 −2.5 −3.2 8 −2.8 −4 −2.8 −4.11.2 −0.1 −2.8 −4.1 0.5 −0.1 −2.5 −3.2 9 −3.4 −4.8 −2.8 −4.1 1.2 −0.1−2.8 −4.1 0.5 −0.1 −2.5 −3.2 10 −4 −5.5 −2.8 −4.1 1.2 −0.1 −2.7 −4.1 0.5−0.1 −2.4 −3.2 11 0 −5.5 −2.8 −4.1 1.2 −0.1 −2.8 −4.1 0.4 −0.1 −2.4 −3.212 3.6 −2.7 −2.8 −4.1 1.2 −0.1 −2.8 −4.1 0.4 −0.1 −2.5 −3.3 13 4.3 −1.6−2.8 −4.1 1.2 −0.1 −2.7 −4.1 0.4 −0.1 −2.4 −3.3 14 5.1 −1 −2.8 −4.1 1.2−0.1 −2.8 −4.1 0.4 −0.1 −2.5 −3.3 15 12.4 8.4 −2.8 −4.1 1.2 −0.2 −2.8−4.1 0.4 −0.1 −2.4 −3.3 16 2.9 3.4 −2.8 −4.1 1.2 −0.1 −2.8 −4.1 0.4 −0.1−2.4 −3.3 17 4.5 3.3 −2.8 −4.1 1.2 −0.1 −2.8 −4.1 0.5 −0.1 −2.4 −3.3 186.5 4.1 −2.8 −4.1 1.2 −0.1 −2.8 −4.1 0.4 −0.1 −2.4 −3.3 19 7.3 4.5 −2.8−4.1 1.2 −0.1 −2.8 −4.1 0.4 −0.1 −2.5 −3.3 20 4.2 3.9 −2.8 −4.1 1.2 −0.1−2.8 −4.1 0.4 −0.1 −2.4 −3.3 21 −0.4 −0.9 −2.8 −4.1 1.2 −0.1 −2.8 −4.10.4 −0.1 −2.5 −3.3 22 −1.1 −2.7 −2.8 −4.1 1.2 −0.1 −2.8 −4.1 0.5 −0.1−2.5 −3.2 23 −2.1 −3.8 −2.8 −4.1 1.2 −0.1 −2.8 −4.1 0.5 −0.1 −2.5 −3.224 −3.4 −4.5 −2.8 −4.1 1.2 −0.1 −2.8 −4.1 0.5 −0.1 −2.5 −3.2 SIM11 SIM12SIM13 Suction Liquid Ambient % RH % Run Cycles −0.8 −1.4 1.7 72 84.4 7554.5 0 2 −0.8 −1.3 1.7 81.5 90 75.1 55.2 0 2 −0.8 −1.3 1.7 81.5 89.775.2 55.3 0 2 −0.8 −1.3 1.7 81.2 89.2 75.3 55 0 2 −0.8 −1.3 1.7 80.688.3 75.3 55.1 0 2 −0.8 −1.3 1.7 80 87.5 75.1 54.8 0 2 −0.8 −1.3 1.779.5 85.3 74.8 54.8 0 2 −0.8 −1.4 1.7 81.2 76.4 74.7 55 0 2 −0.8 −1.41.7 83.2 70.8 74.6 55.3 0 2 −0.8 −1.4 1.7 84.8 71.9 74.7 54.7 0 2 −0.8−1.4 1.7 85.9 73.2 74.8 54.8 0 2 −0.8 −1.4 1.7 86.9 74.6 75 55.7 0 2−0.8 −1.4 1.7 87.6 75.8 75 54.1 0 2 −0.8 −1.4 1.7 88.5 76.8 75.1 55.9 02 −0.8 −1.4 1.7 89.1 77.7 75.1 55.3 0 2 −0.9 −1.4 1.7 78.5 82.5 75.1 5440.1 3 −0.8 −1.3 1.7 80.2 90.4 75.1 51.1 40.1 3 −0.8 −1.3 1.7 80.7 89.175.1 56.7 40.1 3 −0.8 −1.3 1.7 80.5 88.5 75 54.6 40.1 3 −0.9 −1.3 1.780.5 88.3 74.9 56 40.1 3

It can be seen that the regulatory testing data is a rich proprietarydata set upon which numerous data models could be created. For example,it is possible derive a product simulation model that simulates producttemperature by location in the refrigeration appliance as a function ofoperating data such as return air temperature, supply air temperature,suction line temperature liquid line temperature, ambient temperature,ambient relative humidity, and compressor run time. The model could alsobe a three-dimensional model of temperature variation through the insideof the refrigeration appliance. Accordingly, in one or more embodiments,the proprietary OEM data includes a location-specific product simulationmodel for a refrigeration appliance type correlating operating data toproduct temperature to at various locations throughout the refrigerationappliance. As explained more fully below, this enables the asset manager14 to act on operating data by simulating product temperatures at one ormore locations in the refrigeration appliance based on the operatingdata.

Proprietary OEM data can also include one or more models that relateoperating data to life expectancy for one or more components of arefrigeration appliance. Again, such models may be derived fromlong-term experimental testing or other empirical observationtechniques. In one specific example, the OEM database includes modelsorganized by refrigeration appliance type, which correlate liquid linetemperature and ambient temperature to degradation of compressoroperating efficiency. As explained more fully below, access to suchproprietary models enables the asset manager 14 to act on operating datareceived from refrigeration appliances by predicting compressor failurebased on the liquid line temperature and air temperature measurementsreceived from the IOT devices.

Referring to FIGS. 6A-6I, exemplary display screens for a front end webapplication 22 of the asset management system 10 are shown. In FIGS.6A-6I, the display screens are views from the web application 22, but itwill be understood that the mobile application view for mobileapplication 24 can comprise a reformatting of what is shown in FIGS.6A-6I.

FIG. 6A depicts an exemplary embodiment of a fleet overview screen 280for the front end application 22. To display the fleet overview screen280, a user must sign into the front end application. Via the API 240and identity access management system 234, the front end application 22determines the refrigeration appliances assigned to the user andgenerates the fleet overview screen to include information about thoseappliances only. The fleet overview screen 280 is the landing screen forthe front end application 22. It can be seen that the fleet overviewscreen includes an alarm distribution panel 282 providing a summarychart for the alarm status of all of the refrigeration appliancesassigned to this user, an asset status panel 284 providing a summarychart for the asset status of all the refrigeration appliances assignedto this user, a map panel 286 depicting locations of the appliances on amap, and an alarm table panel 288 displaying details about recent alarmindications received from the refrigeration appliances.

FIG. 6B depicts an exemplary embodiment of an asset list view includinga fleet indicator bar 302 and an appliance information table 304. Thefleet indicator bar 302 includes indication objects summarizing thestatus of the fleet. In this case, the left indicator shows the userthat eight refrigeration appliances are assigned. The adjacent indicatorshows that all eight assigned refrigeration appliances are currentlyonline and transmitting data to the asset manager 14. The next indicatorshows that zero appliances are offline. The next indicator shows thatall eight assets have main power. The following indicator shows thatzero appliances are in power failure mode. The second-from-rightindicator shows that zero of the user's refrigeration appliances areunclaimed. And the right-most indicator shows that there are zero activealarm conditions in this fleet of eight appliances. The applianceinformation table 304 provides a summary of information about eachappliance (where each appliance is represented by one row of the table).The information for the table 304 is drawn from the time series database204 and the meta information database 230. From left to right, columnsof the table are labeled ‘Asset’, ‘Location’, ‘Customer’, ‘Store’,‘Asset Type’, and ‘Status’. Temperature display items along theleft-hand side of the table 304 display the current air temperatureinside the refrigeration appliance. As shown, the asset serial number isthe primary identifier in the front end application view 300.

FIG. 6C depicts an exemplary embodiment of an asset filter panel 310.The asset filter panel 310 includes numerous fields for filtering amongthe refrigeration appliances assigned to the user. For example, the usercan filter by appliance type, location, model number, serial number,name, division, and store.

FIG. 6D depicts an exemplary embodiment of a customer view 320. Thecustomer view 320 is available to supervisory administrators, allowingthe administrator to select refrigeration appliances by customer. Thecustomer view 320 also shows the number of divisions, stores, assets,and users that are assigned to each customer, as well as providing alist of actions that are available to the user for each customer.

FIG. 6E depicts an exemplary embodiment of a fleet management view 330.The fleet management view 330 may also be available to administrators ofasset management system accounts. The fleet management view 330 providesa different way of grouping and organizing refrigeration appliances.

FIG. 6F depicts an exemplary embodiment of a store view 340. The storeview 340 allows the user to select appliances by store and displays atable including information about each store, such as location,customer, division, number of assets, number of users, and availableactions.

FIG. 6G depicts an exemplary embodiment of an operating data profilepart view 340. As explained elsewhere, the front end applications 22, 24enable a user to adjust the appliance profile that is loaded into memoryof an appliance's IOT device. The operating data profile part view 340enables a user to view and make changes to the operating data profilepart for various types of refrigeration appliances.

FIG. 6H depicts an exemplary embodiment of an alarm profile part view350. The alarm profile part view 350 enables a user to view and makechanges to the alarm profile part for various types of refrigerationappliances. Note that the alarm profile part view 350 includesinformation about the delay before push notification and types of pushnotifications that are sent in response to the alarm.

FIG. 6I depicts an exemplary embodiment of a user view 360. The userview 360 provides information about the various users of the assetmanagement system 10, their roles, their contact information, thecustomer to which they are assigned, and the store to which they areassigned.

Having described the architecture of the asset management system 10 andits major components in some detail, this disclosure will now turn tothe processes that are enabled by the asset management system.

First, referring to FIG. 7 , an exemplary method of linking arefrigeration appliance to the asset manager 14 is generally indicatedat reference number 400. To connect an IOT device 12 to an appliance,initially wired connections are made to the I/O ports. This process canbe performed during manufacturing of the refrigeration appliance 11 n ina factory-installed application or by a user in the field in a retrofitapplication. In either case, the user is provided with appliancetype-specific instructions for plugging cable connectors of theappliance 11 n into the I/O ports 104A-104F, 106 of the IOT device 12.In accordance with the instructions, the user plugs one or more cableconnectors of the refrigeration appliance 11 n into one or more of (i) aplurality of low voltage I/O ports of the IOT device and (ii) a serialdata port of the I/O device and connects the IOT device to a main powersource MP. The Blues Wireless Notecard 140 automatically connects to theasset management network 20 when the device 12 is powered on. When othertypes of modems are used, it may be necessary for the user to connectthe modem to the network.

Connecting the IOT device 12 to the refrigeration appliance 11 n alsorequires binding the IOT device to the appliance for purposes of assetmanagement communications and data organization. The binding process 400comprises a first step 402 of scanning the QR code 250 on the IOT device12 with a client device, e.g., using the camera on a computer or amobile device such as a smartphone or tablet. As explained above, the QRcode encodes a web address pointing to the data broker and including adevice ID (e.g., IMEI) for the modem 140. The web address may have thefollowing shortened form: https://qrgo.org/id/[devID]. Scanning the QRcode automatically enters the web address into a web browser of theclient device. Thus, as shown in step 404, the client device makes a webpage request to the web address encoded in the QR code, which is the URLcontaining the device ID (e.g., IMEI) of the IOT device 12.

At step 406, the data broker 16 receives the web page request includingthe device ID for the IOT device 12. At step 408, the data broker 16determines based on the device ID contained in the request, whether theIOT device has previously been bound to a refrigeration appliance in theasset management system 10. To make this determination, the data broker16 can query a database it maintains relating device IDs to applianceserial numbers. Alternatively, the data broker 16 could query the metainformation database 230 of the asset manager 14. In this instance,because the IOT device 12 is being bound to the refrigeration appliance12, at decision point 410, the data broker 16 determines that the IOTdevice has not previously been bound to a refrigeration appliance.

As shown in step 412 and FIG. 8 , in response to receiving the web pagerequest for the IOT device 12 and determining that the IOT device hasnot been bound to a refrigeration appliance, the data broker 16 returnsa web form 500 (FIG. 8 ) including a field 502 for input of a serialnumber of a refrigeration appliance. In one or more embodiments,regardless of the result of step 408 and decision point 410, the databroker 16 redirects the client device to a link having the followingexample format:https://true-insight.com/publicAsset?deviceId=[device]&sn=[sn]. When thedata broker 16 determines that the IOT device 12 has not been bound to arefrigeration appliance, the data broker uses DOES_NOT_EXIST for the[sn] field; whereas when the data broker determines that the IOT devicewas previously bound to the refrigeration appliance, the data brokerusers the serial number for the bound appliance in the [sn] field.

The web form 500 displays on the client device, and the user inputs aserial number as shown in step 414. Upon receipt of the serial number,the IOT device 12 is bound to the refrigeration appliance 11 n in theasset management system at step 416. Binding the IOT device 12 to therefrigeration appliance 11 n involves a number of operations.

As shown at 418, binding the IOT device 12 to the refrigerationappliance 11 n comprises configuring the asset manager 14 to parse andstore operating data transmitted by the IOT device based on the serialnumber of the refrigeration appliance.

Additionally, as shown at 420, binding the IOT device 12 to therefrigeration appliance 11 n configures the data broker 16 to receivedata packets from the IOT device containing operating data for therefrigeration appliance and tagged by a device ID for the IOT device andconvert them into structured data objects tagged by the serial number ofthe refrigeration appliance 11 n. Thus, in one embodiment, the bindingprocess configures the data broker 16 to transmit operating data fromthe IOT device 12 as JSON objects, each with a key-value pair for theserial number of the refrigeration appliance 11 n.

As shown at 422, binding the IOT device 12 to the refrigerationappliance 11 n also causes the asset manager 14 to automatically uploadthe appliance profile to the memory of the edge computing device 130. Inone embodiment, the asset manager 14 is configured to query an OEMdatabase 28 for the appliance control profile based on the serial numberof the refrigeration appliance 11 n. As explained above, loading theoperating data part of the appliance control profile into memory of theedge computing device 130 of the IOT device 12 configures the processorof the edge computing device for reading operating data from therespective I/O ports 104A-104F, 106 and transmitting the operating datato the asset manager 14 via the modem 140. Loading the alarm part of theappliance profile further configures the processor for recognizing alarmconditions on the edge of the asset management network 20 andtransmitting alarm indications to the asset manager 14 via the modem140.

Referring to FIGS. 7 and 9 , after the IOT device 12 is bound to therefrigeration appliance 11 n, if a user of a client device scans the QRcode 250 to enter the web address including the device ID (steps 402,303), and the data broker receives the request (step 406) and determinesthat the IOT device has already been bound to the refrigerationappliance 11 n, at step 424, the data broker redirects the client deviceto a web address for the public web page 600 (FIG. 9 ) for therefrigeration appliance. As shown, the public web page 600 displaysoperating data (specifically, air temperature data) for therefrigeration appliance 11 n. The inventor envisions that the same QRcode sticker that is placed at 250 on the IOT device 12 could also beplaced on a customer-accessible location on the refrigeration applianceso that a customer could use the QR code to obtain the public web page600 in order to verify that the product being purchased out of therefrigeration appliance has been held at the proper temperature.

Referring to FIG. 10 , the asset management system 10 in accordance withthe present disclosure can be configured to facilitate a process 700 forseamless replacement of IOT devices 12 without data segmentation. Thesituation addressed here is one in which a first IOT device for a givenrefrigeration appliance 11 n must be replaced with a second IOT device.This situation can arise for any number of reasons, e.g., a malfunctionof the first IOT device, exceeding prepackaged data limits of the firstIOT device, exceeding a lifetime rating of the first IOT device, or anyother reason.

As shown at 702, the starting point for process 700 is a condition inwhich a first IOT device 12 has been bound to the refrigerationappliance 11 n and has transmitted operating data to the asset manager14 for a first period of time. The first IOT device 12 has a firstdevice ID, and the operating data from the appliance 11 n sent via thefirst IOT device was tagged with a serial number for the refrigerationappliance. Since the asset manager 14 is agnostic to the device ID ofthe first IOT device 12, the asset manager 14 has stored operating datafrom the first IOT device 12 in the time series database 204 using theappliance serial number as the primary key.

From this starting point, the first IOT device 12 is disconnected fromthe refrigeration appliance 11 n at step 704, and the second IOT device12 is connected to the refrigeration appliance at step 706. This meansthat cable connectors are removed from the I/O ports 104-104F, 106 ofthe first IOT device 12 and plugged into the same ports of the secondIOT device 12. Subsequently, at step 708, the QR code 250 on the secondIOT device 12 is used to bind the second IOT device to the refrigerationappliance using the above-described binding process 400.

At this stage, the second IOT device 12 has been connected and bound tothe refrigeration appliance 11 n, yet the asset manager 14 has a timeseries database 204 populated with operating data for the refrigerationappliance from the first IOT device 12. At step 710 the second IOTdevice 12 transmits additional operating data for the refrigerationappliance 12 to the asset management network 20. In this case, the databroker 16 tags the operating data from second IOT device 12 with theserial number for the refrigeration appliance 11 n.

At step 712, the asset manager 14 receives tagged operating data andparses the operating data by serial number (714). At the parsing step714, the asset manager 14 parses the operating data from the second IOTdevice 12 in the same way that the asset manager 14 parsed the operatingdata from the first IOT device 12. At step 716, the asset manager 14stores the tagged operating data from the second IOT device 12 in thesame record of the time series database 20 that was used to store theoperating data sent by the first IOT device 12 and has a primary keyequal to the serial number of the appliance. Again, the asset manager 14stores the operating data from the second IOT device 12 in the sameformat that the IOT device stored the operating data from the first IOTdevice. Hence, the asset manager maintains a seamless time seriesdatabase for the refrigeration appliance 11 n encompassing both thefirst operating data transmitted by the first IOT device 12 and thesecond operating data transmitted by the second IOT device 12. Thestructure of the time series database is unchanged by binding the secondIOT device 12 to the refrigeration appliance 11 n.

Accordingly, it can be seen that, in one or more implementations of theasset management system 10, binding an IOT device 12 to a refrigerationappliance 11 n configures the asset manager 14 to store the operatingdata in a preexisting time series database record with the serial numberof the refrigeration appliance as primary key and prepopulated withoperating data for the refrigeration appliance transmitted by anotherIOT device previously bound to the refrigeration appliance.

Referring to FIG. 11 , an edge processing routine that can be conductedby any edge computing device 130 in the asset management system 10 isshown schematically at reference number 800. As shown, the startingpoint 802 for the process 800 is when the IOT device 12 is bound to aparticular refrigeration appliance 12. When binding occurs, theappliance control profile and firmware or other preloaded instructionsstored in the memory of the edge computing device 130 configure theprocessor of the edge computing device to perform a sampling subroutine810, an optional edge compute subroutine 820, an operating datatransmission subroutine 830, and an alarm subroutine 840.

The sampling subroutine 810 configures the edge computing device 130 forsampling operating data from the respective appliance 11 n at a samplingfrequency. In the illustrated embodiment, the sampling subroutine 810 iscontinuous loop of three steps, completed at the sampling frequency.Every sampling interval, the edge computing device 130 wakes up (step812), samples the operating data (step 814), and goes to sleep (step816).

In certain embodiments, the edge computing device 830 is configured toperform one or more edge compute functions 820 on the sampled data on asample-by-sample basis. One example of such an edge compute function 802will be described in reference to FIG. 12 below.

The transmission subroutine 830 configures the edge computing device 130for transmitting the operating data to the asset management network 20via the modem 140 at a transmission frequency less than the samplingfrequency. Typically, the sampling subroutine 810 will collect numerous(e.g., more than 20, more than 50, more than 100, more than 200) samplesbetween each transmission. In the illustrated example, the transmissionsubroutine comprises a batch aggregation step 832 and a datatransmission step 834. In the batch aggregation step 832, the edgecomputing device 130 performs predefined batch aggregation functions onthe sampled data, and optionally on the edge-analyzed data from 820.That is, the edge computing device 130 determines batch values for thesamples collected during the transmission interval. For example, foreach parameter, the edge computing device determines the minimumparameter value during the transmission interval, maximum value, averagevalue, etc. At step 834 the edge computing device 130 transmits theaggregated data to the asset management network 20 as a data packet.

The alarm subroutine 840 configures the edge computing device 130 toanalyze the sampled operating data on the edge of the asset managementnetwork 20 for detecting an alarm condition in the operating data. Ascan be seen, the alarm subroutine 840 is carried out while the edgecomputing device 130 is simultaneously conducting the sampling andtransmission subroutines 810, 830. Moreover, the transmission subroutine830 and the alarm subroutine 840 are taking in and analyzing the sampleddata in parallel. Alarm subroutine 840 comprises a decision point 842 inwhich the edge computing device 130 determines whether the sampled data(or analyzed sampled data from 820) has crossed or otherwise violates analarm threshold. If so, at step 844 the edge computing device 130 isconfigured to immediately transmit an alarm indication to the assetmanagement network 20 via the modem 140. This transmission of the alarmindication is independent of the transmission step 834 in the regularoperating data transmission subroutine 830. Moreover, the alarmindication transmission 844 is asynchronous with respect to thetransmission frequency of the transmission subroutine 830. Because alarmindications are not sent with the regular data packets containingoperating data at the transmission frequency, the IOT device 12employing its edge compute capability is able to notify the assetmanager 14 of all alarms in an actionable timeframe.

Referring to FIG. 12 , a particular implementation of the edgeprocessing routine is shown at 800′. The edge processing routine 800′ isessentially the same as edge processing routine 800, except that an edgecomputing product simulation subroutine 820′ and product temperaturealarm subroutine 840′ are shown in place of the generic edge function820 and alarm subroutine 840. Subroutines and steps of the edgeprocessing routine 800′ that correspond with subroutines and steps ofthe edge processing routine 800 are given the same reference number,followed by a prime symbol.

In edge processing routine 800′, the IOT device 12 analyzes the returnair temperature measured by a return air temperature sensor of therefrigeration appliance 11 n to simulate a product temperature insidethe refrigeration appliance. Those skilled in the art of refrigerationappliances 11 n understand that the air temperature inside therefrigeration appliance and the temperature of product inside therefrigeration appliance will differ, sometimes substantially. But insome refrigeration appliance applications, product temperature iscritical, e.g., scientific coolers/freezers, medical coolers/freezers.The conventional approach to tracking product temperature is to use aproduct simulator probe, like the one used in the regulatory testingexample above. However, the inventor recognizes that the temperature ofa product simulator probe will vary widely depending on location of theprobe in the refrigeration appliance. The inventor believes that in someinstances it would be more effective to make a holistic,location-agnostic estimate by simulating the product temperature as afunction of return air temperature. Moreover, the inventor believes thatthe edge computing capabilities of the IOT device 12 provide the uniquecapability to not only accurately simulate product temperature but toprovide actionable alarms when the simulated product temperature crossesan alarm threshold.

Accordingly, the process 800′ is conducted by an IOT device 12 connectedto a return air temperature sensor of a refrigeration appliance 11 n.The edge computing function 820′ and the edge computing device of the atleast one IOT device is configured to simulate a product temperaturebased on sampled return air temperature from the return air temperaturesensor. Moreover, the edge computing device 130 is configured to updatethe simulation of the product temperature at a simulation frequency thatis much greater than the transmission frequency of transmission process830′. This is a significant advantage of performing the simulation onthe edge of the asset management network 20, rather than on the morepowerful service side of the network, e.g., at the asset manager 14. Inone or more embodiments, the simulation frequency is the same as thesampling frequency. In other words, the edge computing device 130simulates the product temperature on a sample-by-sample basis. As shown,the alarm subroutine 804′ is configured to detect an alarm conditionwhen the simulated product temperature crosses a product temperaturethreshold (842′). Subsequently the edge computing device 130 isconfigured to immediately and asynchronously send an alarm indication tothe asset manager 14.

In an exemplary embodiment, the edge computing device 130 is configuredto simulate the product temperature based on an exponential decay filterthat is a function of the sampled return air temperature. Equation (1)provides an exemplary exponential decay filter algorithm that definesthe relationship between product temperature and time dT/dt as afunction of the air temperature T_(s) of the environment in which theproduct is located:

$\begin{matrix}{\frac{dT}{dt} = {k( {T_{t} - T_{S}} )}} & (1)\end{matrix}$

wherein:

-   -   T_(t) is product temperature;    -   T_(s) is the return air temperature read by the edge computing        device;    -   k is an experimentally derived heat transfer coefficient for the        product.

The exponential decay filter algorithm in Equation (1) can be rewrittento solve for the product temperature over time T(t), as shown inEquation (2) below:

T(t)=T _(s)+(T ₀ −T _(s))e ^(−kt);  (2)

wherein:

-   -   T(t) is the simulated product temperature;    -   T_(s) is the return air temperature read by the edge computing        device;    -   T₀ is an initial product temperature when the product was loaded        into the refrigeration appliance; and    -   k is an experimentally derived heat transfer coefficient for the        product.

In an exemplary embodiment, the edge computing device 130 is configuredto solve for T(t) on a sample-by-sample base (i.e., at the samplingfrequency) or other suitable simulation frequency. In certainembodiments, the initial product temperature T₀ is set to an ambient airtemperature reading from an I/O port when the product was loaded intothe refrigeration appliance. In other embodiments, the initial producttemperature T₀ is input by a user via a front end application 22, 24.Upon receipt of the input of the initial product temperature T₀ to afront end application 22, 24, the asset manager 14 can load the initialproduct temperature into memory of the edge computing device 130.Similarly, the user may use a front end application 22, 24 to select aproduct type. Upon receipt of the product type input, the asset manager14 can look up the heat transfer coefficient k for the product type in alook up table and load the heat transfer coefficient into memory of theedge computing device 130.

Suitably, the simulation frequency is substantially greater than thetransmission frequency. This is important because the accuracy andresponsiveness of Equation (2) depends on frequent updating. It can beseen that, by using the exponential decay filter algorithm to calculatethe simulated product temperature at the IOT device 12 on the edge ofthe IOT network 20, the asset management system 10 is able to obtain aresponsive and accurate simulation of product temperature. Moreover, aholistic, location-agnostic estimate of product temperature is provided.

As shown in FIG. 12 , the alarm subroutine 804′ is configured to detectan alarm condition when the simulated product temperature crosses aproduct temperature threshold (842′) and immediately and asynchronouslysend an alarm indication to the asset manager 14. The asset manager 14is configured to take immediate action in response to the alarmindication (as described below in reference to FIG. 13 ) to push anotification to a user about the alarming product temperature.Accordingly, the asset management system is able to provide pushnotification alarming (within 60 seconds of the alarm conditionoccurring) based on holistic, location-agnostic simulation of producttemperature.

Referring to FIG. 13 , a backend process conducted by the asset managerbackend 200 is generally indicated at reference number 900. At startingpoint 902, the asset manager backend 200 is in communication with aplurality of IOT devices 12 (e.g., more than 10,000 IOT devices) via theasset management network 20. The asset manager 14 configured to streamthe data from all of the IOT devices 12 in the asset management network20. The streamed data includes operating data sent by each IOT device atthe respective transmission frequency and the asynchronous alarmindications.

The ETL system 202 receives the stream of data from the IOT devices 12and parses the data objects (both operating data objects and alarmindications) by serial number of the source refrigeration appliance(step 904). Additionally, the ETL system parses the data objects by dataobject type (step 906). This step leads to decision point 908, whereinthe ETL system 202 determines whether each data object is an alarmindication. If not, the ETL system 202 performs required datatransformations and loads the operating data into the time seriesdatabase 204 (step 910). The operating data from each such data objectis loaded into a record of the time series database 204 having theserial number of the refrigeration appliance as primary key.

If at decision point 908 the ETL system 202 determines that a dataobject is an alarm indication, it recognizes the alarm indication as anevent requiring event driven processing and immediately sends the alarmindication to the alarm service module 214 (step 912). At step 914 anddecision point 916, the alarm service module 214 applies rules-basedlogic to assess whether a push notification is required. The rules-basedlogic may require consulting the alarm information previously stored incache 217. For example, the rules-based logic can define a time intervalthat the alarm condition must be maintained before push notificationabout the alarm indication is required, and the assessment can be madeby referencing cached information about how long the alarm condition hasbeen maintained. If push notification is not immediately requiredaccording to the rules-based logic, the alarm service module 214 makes afurther determination at decision point 918 of whether the alarmindication should be stored in temporary cache 217 for subsequentapplication of the rules-based logic. If yes, the alarm service module214 stores the alarm indication in cache 217 at step 920; and if no, thealarm service module 214 simply ignores the alarm at step 922.

When the alarm service module 214 determines at step 916 that a pushnotification is required, at step 924 the alarm service module sends thealarm indication to the push notification service module 216. At step926, the push notification service module applies push notificationrules to determine push notification address(s) to receive alarms. Forexample, the push notification service module 216 can reference metainformation stored in the meta information database 230 to apply rulesthat define, for example, which type of user (e.g., maintenance person,store employee, store manager, appliance owner, etc.) receives a pushnotification alarm for a given alarm condition type (e.g., door openalarm sent to store employee user type, over-temperature alarm sent tostore manager user type, compressor failure alarm sent to maintenanceperson user type, etc.), how many push notification alarms of a givenalarm condition type (e.g., main power failure alarms) should be sent toa user from multiple refrigeration appliances at a given store orlocation, etc. After applying the rules-based logic, the pushnotification service module 216 sends push notifications via SMS textmessage and/or email to designated users.

Referring to FIGS. 11 and 13 , it can be seen that the IOT device edgecomputing process 800 and the asset manager backend process 900cooperate to provide a push notification alarm system that isfundamentally different than conventional push notification alarmsystems proposed for refrigeration appliances. In conventional systems,the remote asset manager receives operating data from the appliances atthe normal transmission frequency and parses the data at thetransmission frequency to determine whether push notification isrequired. That is, the server side is responsible for executing theentire action sequence in response to an alarm condition. However, theinventor recognizes that data limitations require transmission frequencyto be relatively slow. For example, it is common in the industry totransmit operating data every 30 minutes, every hour, every 4 hours,every 8 hours, or every 24 hours. So if an alarm condition (e.g., dooropen) occurs at the beginning of a transmission interval it can havesevere adverse consequences on the refrigeration appliance or productcontained therein before the asset manager ever even receives operatingdata indicating the alarm condition has occurred. But by bifurcating thealarm response functions between the edge computing devices 130 and theasset manager backend 202, the illustrated asset manager is able toremedy this issue. Because each IOT device is configured to recognizealarms on the edge of the IOT network 20 and send the alarmsasynchronously of the normal transmission frequency in a parallel alarmroutine 840, the asset manager backend process 900 is able to act on thealarm in a timely fashion. Every alarm condition for which pushnotification is required is pushed to the designated users within 60seconds of the alarm condition occurring. This makes the pushnotification alarms truly actionable, yielding greater appliance up-timeand less loss of product.

Referring to FIG. 14 , additional aspects of the present disclosurerelate to applications that employ the assent-centric architecturedescribed above to enrich the operating data obtained from therefrigeration appliances 11A-11 n. In general, these aspects leveragehow the above-described asset management system 10 associates operatingdata directly with the serial number for the source refrigerationappliance. This enables an asset manager 14 to utilize the richproprietary data stored in the OEM database 28 (and organized byrefrigeration appliance type) to (i) determine the type of refrigerationappliance based the serial number and (ii) combine the operating datafor the refrigeration appliance with the rich proprietary data set forthe respective refrigeration appliance type to yield better assessmentsof the asset operating data. For example, a manufacturer of assets mighthave extensive simulation, empirical testing, or modeled data about themany types of assets it produces. Because the serial number for eachasset is provided along with the asset operating data, a manufacturercan easily determine which proprietary data applies to the asset andthen combine the relevant proprietary data with the operating data toyield improved predictive analytic outputs, improved simulation outputs,etc.

This concept is schematically illustrated in FIG. 14 , where referencenumber 1010 represents the asset manager 14 receiving applianceoperating data and storing it in the time series database 204 usingappliance serial number as primary key. Receipt of proprietary datastored in the OEM database 28 is indicated at reference number 1020. Thetwo data sets received in steps 1010, 1020 are combined at referencenumber 1030 to yield enriched analysis of the appliance operating dataat 1040. Accordingly, in one or more embodiments, the asset manager 14is configured to read the proprietary OEM data 1020 from the OEMdatabase 28 and to act on the operating data for at least onerefrigeration appliance of a specified refrigeration appliance type bycombining (1030) the operating data for the appliance of the specifiedtype with the proprietary OEM data for the specified appliance type toobtain a predictive analytic or simulation output 104 for therefrigeration appliance.

Referring to FIG. 15 , in one particular implementation, the dataenrichment process is used in a compressor life prediction process 1100.In this embodiment, at step 1110 the asset manager 14 receives from therefrigeration appliance operating data including liquid line temperatureand air temperature (e.g., return air temperature), wherein theoperating data is tagged by the appliance serial number. At step 1120,the asset manager 14 reads proprietary data from the OEM database 28 forthe refrigeration appliance type determined based on serial number. Inparticular, the proprietary data includes a model that correlates liquidline temperature and air temperature to degradation of compressoroperating efficiency. As explained above, such a model can be obtainedthrough experimental testing of refrigeration appliances of thespecified type and or through long term observational studies of howrefrigeration appliances of the specified type perform in the field(e.g., observation of the refrigeration appliances can be carried out bythe asset manager 14 as discussed below). At step 1130, the assetmanager 14 combines the operating data from step 1110 with the datamodel from step 1120 to predict the remaining compressor life (step1140). Based on the compressor life prediction determined in step 1140,the asset manager can take additional action, such as pushing an alarmnotification to a user, notifying a maintenance person that compressormaintenance is required, etc.

Referring to FIG. 16 , in another implementation, the data enrichmentprocess comprises a location-specific temperature simulation process1200. In this embodiment, at step 1210 the asset manager 14 receivesoperating data tagged by serial number. The operating data includes atleast a return air temperature. In some cases, the operating datafurther includes compressor cycle data, refrigerant temperature data, orother temperature data. In an exemplary embodiment, at step 1210, theasset manager 12 further receives information from the user about thetype of product contained in the refrigeration appliance. As explainedabove, the front end applications 22, 24 may facilitate user input ofthis information.

As explained above, the OEM database 28 can store a three-dimensionaltemperature model for temperature inside the refrigeration appliance. Inthis embodiment, the asset manager 14 receives the location-specifictemperature simulation model at step 1220. By combing (step 1230) thelocation-specific temperature simulation model 1220 with the operatingdata and product information obtained in step 1210, the asset manager 14is configured to act on the operating data by simulating air or producttemperatures at one or more locations in the refrigeration appliance.For example, the asset manager 14 can input the operating data into thethree-dimensional air temperature model to track air temperature as afunction of location inside the refrigeration appliance over time. Thisinformation, in turn, could be used in combination with the exponentialdecay filter algorithm described above to simulate product temperatureat particular locations of interest within the refrigeration appliance.Alternatively, the location-specific temperature model may directlymodel product temperature in relation to three-dimensional locationsinside the refrigeration appliance. In this case, the exponential decayfilter algorithm would not be required to provide an estimate of producttemperature.

Referring to FIG. 17 , the present disclosure also contemplatesleveraging the asset-centric system architecture to enrich the data setof the asset manager 14 or OEM database 28. In particular, because theoperating data for all the refrigeration appliances reporting to theasset manager are tagged by the appliance serial number and stored in atime series database 204 using the serial number as primary key, theasset manager 14 can group the data by refrigeration appliance type andthereby generate a deep and rich data set for each type of refrigerationappliance under management. Using various big data techniques, the assetmanager 14 can employ the enriched data set to iteratively improve anyanalysis of the operating data on a refrigeration appliance type basis.

An exemplary process for improving appliance fleet analytics is shown atreference number 1300 in FIG. 17 . At step 1302, the asset manager 14receives operating data (tagged by serial number) for the entire fleetof appliances 11A-11 n. In step 1304, the asset manager then aggregatesthis (massive) data set. The aggregation step 1304 can comprisedetermining subsets of the refrigeration appliances that are the sametype and grouping the operating data by refrigeration appliance type.Once the operating data is aggregated by appliance type, in step 1306the asset manager 14 is configured to analyze the aggregated data toobtain new data models and/or simulations that relate applianceoperating data to expected outcomes.

Referring to FIG. 18 , in one particular embodiment, the asset manager14 is configured to conduct a process 1400 for generating a predictivemaintenance or predictive failure model using machine learning trainedon the operating data obtained from the refrigeration appliance fleet.Initially at step 1402 the asset manager 14 receives operating data(tagged by serial number) for the entire fleet of appliances 11A-11 nover a period of time (e.g., 1 month, three months, 6 months, 1 year, 2years, etc.). In step 1402, the asset manager 14 can also record thealarm indications received over the same period of time and organize thealarm indications in a database by serial number. In step 1403, theasset manager 14 further receives maintenance and repair informationabout the appliance fleet over the same period of time. This maintenanceand repair information can be obtained by service technicians inputtinginformation to a front end application 22, 24 about each maintenance andrepair they performed on an appliance, identifying the appliance serialnumber. Additionally, the data in step 1403 can be obtained by the OEMfrom warranty claims. At step 1404, the asset manager aggregates theoperating data, alarm data, and maintenance and repair information byappliance type. Again, this step 1404 comprises determining subsets ofthe refrigeration appliances that are the same type and grouping thevarious data by refrigeration appliance type.

Subsequently, in step 1405, the asset manager 14 or another softwareservice module trains a machine learning model to recognize patterns inthe aggregated operating data for refrigeration appliance types that arepredictive of subsequent alarm indications and/or unscheduledmaintenance or repair events. Then, based on this machine learningmodel, the asset manager 14 or another software service module cancreate new alarm profiles that provide indication when operating data ispredictive of an imminent maintenance or repair event. Additionally, theOEM can use the machine learning model to implement manufacturing ordesign improvements to its refrigeration appliances to improve thereliability of subsequently manufactured and/or designed appliances.

In view of the foregoing, it can be seen that the present disclosureprovides asset management systems, IOT devices, and related processesthat facilitate effective remote monitoring of refrigeration appliances.The inventor believes that the technology described herein improves onprior art asset management systems and IOT devices by employing anappliance-focused communication scheme and network architecture.Additionally, the IOT devices disclosed herein are thought to improve onprior art IOT gateway devices by being capable of direct integrationwith virtually any refrigeration appliance. The IOT devices and serverapplications cooperate to facilitate simple binding to refrigerationappliance assets and to provide truly actionable edge alarmcapabilities. Furthermore, the novel data structures described herein,which put the appliance at the center of the data organization scheme,enable enriched analysis of refrigeration appliance operating data forimproved reliability and performance over the life of the appliance.Additionally, the novel data structures organized by appliance serialnumber allow for big data techniques to be employed on massive amountsof operating data taken from numerous appliances to improve theanalytical models used for various refrigeration appliance types, and toimprove the design and manufacture of future refrigeration appliances.Still furthermore, aspects of the present disclosure have been shown toprovide effective means for handling refrigeration appliance operatingdata in the event of power outage, as well as to provide a holistic,location-agnostic simulation of product temperature when refrigerationappliances are used to keep sensitive products at temperature.

Embodiments of the present disclosure may comprise a special purposecomputer including a variety of computer hardware, as described ingreater detail herein.

For purposes of illustration, programs and other executable programcomponents may be shown as discrete blocks. It is recognized, however,that such programs and components reside at various times in differentstorage components of a computing device, and are executed by a dataprocessor(s) of the device.

Although described in connection with an example computing systemenvironment, embodiments of the aspects of the invention are operationalwith other special purpose computing system environments orconfigurations. The computing system environment is not intended tosuggest any limitation as to the scope of use or functionality of anyaspect of the invention. Moreover, the computing system environmentshould not be interpreted as having any dependency or requirementrelating to any one or combination of components illustrated in theexample operating environment. Examples of computing systems,environments, and/or configurations that may be suitable for use withaspects of the invention include, but are not limited to, personalcomputers, server computers, cloud computing services, hand-held orlaptop devices, multiprocessor systems, microprocessor-based systems,set top boxes, programmable consumer electronics, mobile telephones,network PCs, minicomputers, mainframe computers, distributed computingenvironments that include any of the above systems or devices, and thelike.

Embodiments of the aspects of the present disclosure may be described inthe general context of data and/or processor-executable instructions,such as program modules, stored one or more tangible, non-transitorystorage media and executed by one or more processors or other devices.Generally, program modules include, but are not limited to, routines,programs, objects, components, and data structures that performparticular tasks or implement particular abstract data types. Aspects ofthe present disclosure may also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed computingenvironment, program modules may be located in both local and remotestorage media including memory storage devices.

In operation, processors, computers and/or servers may execute theprocessor-executable instructions (e.g., software, firmware, and/orhardware) such as those illustrated herein to implement aspects of theinvention.

Embodiments may be implemented with processor-executable instructions.The processor-executable instructions may be organized into one or moreprocessor-executable components or modules on a tangible processorreadable storage medium. Also, embodiments may be implemented with anynumber and organization of such components or modules. For example,aspects of the present disclosure are not limited to the specificprocessor-executable instructions or the specific components or modulesillustrated in the figures and described herein. Other embodiments mayinclude different processor-executable instructions or components havingmore or less functionality than illustrated and described herein.

The order of execution or performance of the operations in accordancewith aspects of the present disclosure illustrated and described hereinis not essential, unless otherwise specified. That is, the operationsmay be performed in any order, unless otherwise specified, andembodiments may include additional or fewer operations than thosedisclosed herein. For example, it is contemplated that executing orperforming a particular operation before, contemporaneously with, orafter another operation is within the scope of the invention.

When introducing elements of the invention or embodiments thereof, thearticles “a,” “an,” “the,” and “said” are intended to mean that thereare one or more of the elements. The terms “comprising,” “including,”and “having” are intended to be inclusive and mean that there may beadditional elements other than the listed elements.

Not all of the depicted components illustrated or described may berequired. In addition, some implementations and embodiments may includeadditional components. Variations in the arrangement and type of thecomponents may be made without departing from the spirit or scope of theclaims as set forth herein. Additional, different or fewer componentsmay be provided and components may be combined. Alternatively, or inaddition, a component may be implemented by several components.

The above description illustrates embodiments by way of example and notby way of limitation. This description enables one skilled in the art tomake and use aspects of the invention, and describes severalembodiments, adaptations, variations, alternatives and uses of theaspects of the invention, including what is presently believed to be thebest mode of carrying out the aspects of the invention. Additionally, itis to be understood that the aspects of the invention are not limited inits application to the details of construction and the arrangement ofcomponents set forth in the following description or illustrated in thedrawings. The aspects of the invention are capable of other embodimentsand of being practiced or carried out in various ways. Also, it will beunderstood that the phraseology and terminology used herein is for thepurpose of description and should not be regarded as limiting.

It will be apparent that modifications and variations are possiblewithout departing from the scope of the invention defined in theappended claims. As various changes could be made in the aboveconstructions and methods without departing from the scope of theinvention, it is intended that all matter contained in the abovedescription and shown in the accompanying drawings shall be interpretedas illustrative and not in a limiting sense.

In view of the above, it will be seen that several advantages of theaspects of the invention are achieved and other advantageous resultsattained.

The Abstract and Summary are provided to help the reader quicklyascertain the nature of the technical disclosure. They are submittedwith the understanding that they will not be used to interpret or limitthe scope or meaning of the claims. The Summary is provided to introducea selection of concepts in simplified form that are further described inthe Detailed Description. The Summary is not intended to identify keyfeatures or essential features of the claimed subject matter, nor is itintended to be used as an aid in determining the claimed subject matter.

1-91. (canceled)
 92. An asset management system for refrigerationappliances comprising: a plurality of IOT devices, each IOT devicecomprising an edge computing device and a modem for connecting the IOTdevice to an asset management network, each IOT device being connectedto a respective refrigeration appliance and the edge computing devicebeing configured for sampling operating data from the respectiverefrigeration appliance at a sampling frequency, the edge computingdevice being further configured for transmitting the operating data tothe asset management network via the modem at a transmission frequencyless than the sampling frequency, the edge computing device furtherconfigured to analyze the sampled operating data on an edge of the assetmanagement network for detecting an alarm condition in the operatingdata, the edge computing device further configured to immediatelytransmit an alarm indication to the asset management network via themodem when the alarm condition is detected in the operating data, theedge computing device being configured to transmit the alarm indicationasynchronously from the transmission frequency; and an asset manager incommunication with the plurality of IOT devices via the asset managementnetwork, the asset manager configured to receive a data stream from theIOT devices including the operating data and the alarm indications, theasset manager configured to recognize each alarm indication as an eventand immediately conduct event driven processing to assess whether pushnotification is required and push one or more notifications to one ormore users when push notification is required.
 93. The asset managementsystem of claim 92, wherein the asset management system is configured topush said one or more notifications to said one or more users within 60seconds of the alarm condition occurring at the refrigeration appliance.94. The asset management system of claim 93, wherein the plurality ofIOT devices is at least 10,000 IOT devices.
 95. The asset managementsystem of claim 92, wherein the asset manager comprises an ETL systemand an alarm service module, the ETL system configured to extract eachalarm indication in the data stream and immediately send the alarmindication to the alarm service module.
 96. The asset management systemof claim 95, wherein the alarm service module is configured to executerules-based logic to determine whether push notification is required.97. The asset management system of claim 96, wherein the asset managerinterfaces with a front end application configured to facilitate useradjustment of the rules-based logic.
 98. The asset management system ofclaim 96, wherein the rules-based logic defines one or more timeintervals the alarm condition must toll before push notification aboutthe alarm indication is required.
 99. The asset management system ofclaim 98, wherein the asset manager further comprises a cache in whichthe alarm service stores temporary information about alarm indicationsfor evaluating the rules-based logic.
 100. The asset management systemof claim 96, wherein the asset manager further comprises a pushnotification service module, the alarm service module being configuredto notify the push notification service module when push notification isrequired for an alarm indication.
 101. The asset management system ofclaim 100, wherein the push notification service module is configured toexecute rules-based logic to determine one or more notificationaddresses to receive push notification about the alarm condition andsubsequently push the notification to the one or more notificationaddresses.
 102. The asset management system of claim 101, wherein thepush notification service module is configured to send pushnotifications as either of SMS text messages and emails.
 103. The assetmanagement system of claim 101, wherein the asset manager furthercomprises a meta information database storing meta information for eachof the refrigeration appliances, wherein the push notification servicemodule is configured to reference the meta information for therespective refrigeration appliance and execute the rules-based logicbased on the meta information to determine the one or more notificationaddresses.
 104. The asset management system of claim 103, wherein themeta information includes information about types of users for eachrefrigeration appliance and wherein the push notification service moduleis configured to execute rules-based logic defining which types of theusers are to receive push notification based on a type of alarmcondition.
 105. The asset management system of claim 103, wherein themeta information includes information about a store where eachrefrigeration appliance is deployed and wherein the push notificationservice module is configured to execute rules-based logic defining anumber of push notifications to send to a push notification address whenalarm indications are received from a plurality of refrigerationappliances deployed at a same store.
 106. The asset management system ofclaim 101, wherein the asset manager interfaces with a front endapplication configured to facilitate user adjustment of the rules-basedlogic for the push notification service module.
 107. The assetmanagement system of claim 92, wherein at least one IOT device isconnected to an air temperature sensor of the respective refrigerationappliance and the edge computing device of the at least one IOT deviceis configured to simulate a product temperature based on sampled airtemperature from the air temperature sensor.
 108. The asset managementsystem of claim 107, wherein the edge computing device of the at leastone IOT device is configured to update simulation of the producttemperature at the sampling frequency.
 109. The asset management systemof claim 107, wherein the edge computing device of the at least one IOTdevice is configured to detect an alarm condition when the simulatedproduct temperature crosses a product temperature threshold. (Original)110. The asset management system of claim 107, wherein the edgecomputing device of the at least one IOT device is configured tosimulate the product temperature based on an exponential decay filter,the exponential decay filter being a function of the sampled airtemperature.
 111. A method of using an asset management system for aplurality of refrigeration appliances, each refrigeration appliancebeing bound to an IOT device including an edge computing device and amodem, the asset management system including an asset management networkconnecting the IOT devices to a remote asset manager, the methodcomprising: sampling at a sampling frequency operating data from eachrefrigeration appliance at the edge computing device of the respectiveIOT device; transmitting at a transmission frequency the operating datafrom each refrigeration appliance from the modem of the respective IOTdevice to the asset manager via the asset management network, thetransmission frequency being less than the sampling frequency; receivinga stream of the transmitted operating data from the IOT devices at theasset manager; loading by the asset manager the operating data into atime series database; while performing said sampling and saidtransmitting, detecting at the edge computing device of one of the IOTdevices an alarm condition in the operating data; immediately sending analarm indication from the modem of said one of the IOT devices to theasset manager via the asset management network, said immediately sendingbeing independent of said transmitting of the operating data from themodem of said one of the IOT devices and being asynchronous with respectto the transmission frequency for said one of the IOT devices; receivingthe alarm indication at the asset manager; and using event drivenprocessing at the asset manager, determining that the alarm indicationrequires push notification and immediately pushing one or morenotifications to one or more users of the refrigeration appliance boundto said one of the IOT devices. 112-164. (canceled)